General Setup


Create a new analysis directory...
[1] FALSE
[1] FALSE
[1] FALSE
[1] FALSE
[1] FALSE
[1] "/Users/swvanderlaan/git/CirculatoryHealth/AE_20211201_YAW_SWVANDERLAAN_HDAC9"
 [1] "_archived"                                     "1. AE_20211201_YAW_SWVANDERLAAN_HDAC9.nb.html" "1. AE_20211201_YAW_SWVANDERLAAN_HDAC9.Rmd"    
 [4] "2. SNP_analyses.Rmd"                           "20220317.HDAC9.AESCRNA.results.RData"          "20220317.HDAC9.baseline.RData"                
 [7] "3.1 bulkRNAseq.preparation.nb.html"            "3.1 bulkRNAseq.preparation.Rmd"                "3.2 bulkRNAseq.main_analysis.nb.html"         
[10] "3.2 bulkRNAseq.main_analysis.Rmd"              "3.3 bulkRNAseq.additional_figures.Rmd"         "3.4 bulkRNAseq.review_comments.Rmd"           
[13] "4. report.scrnaseq.nb.html"                    "4. report.scrnaseq.Rmd"                        "AE_20211201_YAW_SWVANDERLAAN_HDAC9.Rproj"     
[16] "AnalysisPlan"                                  "HDAC9"                                         "images"                                       
[19] "LICENSE"                                       "README.html"                                   "README.md"                                    
[22] "references.bib"                                "renv"                                          "renv.lock"                                    
[25] "scripts"                                       "SNP"                                           "targets"                                      
source(paste0(PROJECT_loc, "/scripts/functions.R"))
install.packages.auto("pander")
Loading required package: pander
install.packages.auto("readr")
Loading required package: readr
install.packages.auto("optparse")
Loading required package: optparse
install.packages.auto("tools")
Loading required package: tools
install.packages.auto("dplyr")
Loading required package: dplyr

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
install.packages.auto("tidyr")
Loading required package: tidyr
install.packages.auto("naniar")
Loading required package: naniar
# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     
data.table 1.14.2 using 1 threads (see ?getDTthreads).  Latest news: r-datatable.com
**********
This installation of data.table has not detected OpenMP support. It should still work but in single-threaded mode.
This is a Mac. Please read https://mac.r-project.org/openmp/. Please engage with Apple and ask them for support. Check r-datatable.com for updates, and our Mac instructions here: https://github.com/Rdatatable/data.table/wiki/Installation. After several years of many reports of installation problems on Mac, it's time to gingerly point out that there have been no similar problems on Windows or Linux.
**********

Attaching package: 'data.table'

The following objects are masked from 'package:dplyr':

    between, first, last
install.packages.auto("tidyverse")
Loading required package: tidyverse
── Attaching packages ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✓ ggplot2 3.3.5     ✓ stringr 1.4.0
✓ tibble  3.1.6     ✓ forcats 0.5.1
✓ purrr   0.3.4     
── Conflicts ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x data.table::between() masks dplyr::between()
x dplyr::filter()       masks stats::filter()
x data.table::first()   masks dplyr::first()
x dplyr::lag()          masks stats::lag()
x data.table::last()    masks dplyr::last()
x purrr::transpose()    masks data.table::transpose()
install.packages.auto("knitr")
Loading required package: knitr
install.packages.auto("DT")
Loading required package: DT
Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio
install.packages.auto("eeptools")
Loading required package: eeptools
Welcome to eeptools for R version 1.2.0!
Developed by Jared E. Knowles 2012-2018
for the Wisconsin Department of Public Instruction
Distributed without warranty.
install.packages.auto("openxlsx")
Loading required package: openxlsx
install.packages.auto("haven")
Loading required package: haven
install.packages.auto("tableone")
Loading required package: tableone
install.packages.auto("sjPlot")
Loading required package: sjPlot
install.packages.auto("BlandAltmanLeh")
Loading required package: BlandAltmanLeh
# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')
Loading required package: devtools
Loading required package: usethis
# for plotting
install.packages.auto("pheatmap")
Loading required package: pheatmap
install.packages.auto("forestplot")
Loading required package: forestplot
Loading required package: grid
Loading required package: magrittr

Attaching package: 'magrittr'

The following object is masked from 'package:purrr':

    set_names

The following object is masked from 'package:tidyr':

    extract

Loading required package: checkmate
install.packages.auto("ggplot2")

install.packages.auto("ggpubr")
Loading required package: ggpubr
install.packages.auto("UpSetR")
Loading required package: UpSetR
devtools::install_github("thomasp85/patchwork")
Using github PAT from envvar GITHUB_PAT
Skipping install of 'patchwork' from a github remote, the SHA1 (79223d30) has not changed since last install.
  Use `force = TRUE` to force installation
# for Seurat etc
install.packages.auto("GenomicFeatures")
Loading required package: GenomicFeatures
Loading required package: BiocGenerics

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:dplyr':

    combine, intersect, setdiff, union

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    anyDuplicated, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep, grepl, intersect,
    is.unsorted, lapply, Map, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rownames, sapply, setdiff,
    sort, table, tapply, union, unique, unsplit, which.max, which.min

Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

The following objects are masked from 'package:data.table':

    first, second

The following object is masked from 'package:tidyr':

    expand

The following objects are masked from 'package:dplyr':

    first, rename

The following objects are masked from 'package:base':

    expand.grid, I, unname

Loading required package: IRanges

Attaching package: 'IRanges'

The following object is masked from 'package:purrr':

    reduce

The following object is masked from 'package:data.table':

    shift

The following objects are masked from 'package:dplyr':

    collapse, desc, slice

Loading required package: GenomeInfoDb
Loading required package: GenomicRanges
Loading required package: AnnotationDbi
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for packages
    'citation("pkgname")'.


Attaching package: 'Biobase'

The following object is masked from 'package:checkmate':

    anyMissing


Attaching package: 'AnnotationDbi'

The following object is masked from 'package:dplyr':

    select
install.packages.auto("GenomicRanges")
install.packages.auto("SummarizedExperiment")
Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats

Attaching package: 'matrixStats'

The following objects are masked from 'package:Biobase':

    anyMissing, rowMedians

The following object is masked from 'package:checkmate':

    anyMissing

The following object is masked from 'package:dplyr':

    count


Attaching package: 'MatrixGenerics'

The following objects are masked from 'package:matrixStats':

    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, colCounts, colCummaxs, colCummins, colCumprods, colCumsums, colDiffs, colIQRDiffs, colIQRs,
    colLogSumExps, colMadDiffs, colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats, colProds, colQuantiles, colRanges, colRanks, colSdDiffs,
    colSds, colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads, colWeightedMeans, colWeightedMedians, colWeightedSds, colWeightedVars, rowAlls,
    rowAnyNAs, rowAnys, rowAvgsPerColSet, rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs,
    rowLogSumExps, rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins, rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks, rowSdDiffs,
    rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, rowWeightedMads, rowWeightedMeans, rowWeightedMedians, rowWeightedSds, rowWeightedVars

The following object is masked from 'package:Biobase':

    rowMedians
install.packages.auto("DESeq2")
Loading required package: DESeq2
install.packages.auto("org.Hs.eg.db")
Loading required package: org.Hs.eg.db
install.packages.auto("mygene")
Loading required package: mygene
install.packages.auto("TxDb.Hsapiens.UCSC.hg19.knownGene")
Loading required package: TxDb.Hsapiens.UCSC.hg19.knownGene
install.packages.auto("org.Hs.eg.db")
install.packages.auto("AnnotationDbi")
install.packages.auto("EnsDb.Hsapiens.v86")
Loading required package: EnsDb.Hsapiens.v86
Loading required package: ensembldb
Loading required package: AnnotationFilter

Attaching package: 'AnnotationFilter'

The following object is masked from 'package:magrittr':

    not


Attaching package: 'ensembldb'

The following object is masked from 'package:openxlsx':

    addFilter

The following object is masked from 'package:dplyr':

    filter

The following object is masked from 'package:stats':

    filter
install.packages.auto("EnhancedVolcano")
Loading required package: EnhancedVolcano
Loading required package: ggrepel
Registered S3 methods overwritten by 'ggalt':
  method                  from   
  grid.draw.absoluteGrob  ggplot2
  grobHeight.absoluteGrob ggplot2
  grobWidth.absoluteGrob  ggplot2
  grobX.absoluteGrob      ggplot2
  grobY.absoluteGrob      ggplot2

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
###
### No. Color                 HEX   (RGB)                                     CHR         MAF/INFO
###---------------------------------------------------------------------------------------
### 1     yellow                #FBB820 (251,184,32)                      =>    1       or 1.0>INFO
### 2     gold                #F59D10 (245,157,16)                    =>    2       
### 3     salmon                #E55738 (229,87,56)                   =>    3       or 0.05<MAF<0.2 or 0.4<INFO<0.6
### 4     darkpink          #DB003F ((219,0,63)                   =>    4       
### 5     lightpink         #E35493 (227,84,147)                      =>    5       or 0.8<INFO<1.0
### 6     pink                #D5267B (213,38,123)                    =>    6       
### 7     hardpink          #CC0071 (204,0,113)                   =>    7       
### 8     lightpurple       #A8448A (168,68,138)                      =>    8       
### 9     purple                #9A3480 (154,52,128)                      =>    9       
### 10  lavendel            #8D5B9A (141,91,154)                      =>    10      
### 11  bluepurple        #705296 (112,82,150)                    =>    11      
### 12  purpleblue        #686AA9 (104,106,169)               =>    12      
### 13  lightpurpleblue #6173AD (97,115,173/101,120,180)    =>  13      
### 14  seablue             #4C81BF (76,129,191)                      =>    14      
### 15  skyblue             #2F8BC9 (47,139,201)                      =>    15      
### 16  azurblue            #1290D9 (18,144,217)                      =>    16      or 0.01<MAF<0.05 or 0.2<INFO<0.4
### 17  lightazurblue     #1396D8 (19,150,216)                    =>    17      
### 18  greenblue           #15A6C1 (21,166,193)                      =>    18      
### 19  seaweedgreen      #5EB17F (94,177,127)                    =>    19      
### 20  yellowgreen       #86B833 (134,184,51)                    =>    20      
### 21  lightmossgreen  #C5D220 (197,210,32)                      =>    21      
### 22  mossgreen           #9FC228 (159,194,40)                      =>    22      or MAF>0.20 or 0.6<INFO<0.8
### 23  lightgreen      #78B113 (120,177,19)                      =>    23/X
### 24  green                 #49A01D (73,160,29)                     =>    24/Y
### 25  grey                  #595A5C (89,90,92)                        =>  25/XY   or MAF<0.01 or 0.0<INFO<0.2
### 26  lightgrey           #A2A3A4 (162,163,164)                 =>    26/MT
###
### ADDITIONAL COLORS
### 27  midgrey         #D7D8D7
### 28  verylightgrey   #ECECEC"
### 29  white           #FFFFFF
### 30  black           #000000
###----------------------------------------------------------------------------------------------

uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
                 "#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
                 "#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
                 "#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
                 "#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
                        "#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
                        "#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
                        "#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
                        "#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
                        "#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")
### ----------------------------------------------------------------------------

ERA-CVD ‘druggable-MI-targets’

For the ERA-CVD ‘druggable-MI-targets’ project (grantnumber: 01KL1802) we performed two related RNA sequencing (RNAseq) experiments:

  1. conventional (‘bulk’) RNAseq using RNA extracted from carotid plaque samples, n ± 700. As of Friday, March 18, 2022 all samples have been selected and RNA has been extracted; quality control (QC) was performed and we have a dataset of 635 samples.

  2. single-cell RNAseq (scRNAseq) of at least n = 40 samples (20 females, 20 males). As of Friday, March 18, 2022 data is available of 40 samples (3 females, 15 males), we are extending sampling to get more female samples.

Plaque samples are derived from carotid endarterectomies as part of the Athero-Express Biobank Study which is an ongoing study in the UMC Utrecht.

Background

Here we obtain data from the HDAC9 in plaques.

library(openxlsx)

gene_list_df <- read.xlsx(paste0(PROJECT_loc, "/targets/Genes.xlsx"), sheet = "Genes")

target_genes <- unlist(gene_list_df$Gene)
target_genes
[1] "HDAC9"  "TWIST1" "IL6"    "IL1B"  

Load data

First we will load the data:

  • bulk RNA sequencing (RNAseq) experimental data from carotid plaques
  • Athero-Express clinical data.

Bulk RNAseq data

Here we load the latest dataset from our Athero-Express bulk RNA experiment d.d. 2021-12-03 mapped to b37 and Ensembl 87.

These bulk RNAseq data are filtered and corrected:

  • UMI corrected
  • unmappable genes are excluded
# bulk RNAseq data
bulkRNA_counts_raw_qc_umicorr <- fread(paste0(AERNA_loc,"/raw_data_bulk/raw_counts_batch1till11_qc_umicorrected.txt"))

# batch information
bulkRNA_meta <- fread(paste0(AERNA_loc,"/raw_data_bulk/metadata_raw_counts_batch1till11.txt"))

Quick peek at the counts and meta-data of the RNAseq experiment.


head(bulkRNA_counts_raw_qc_umicorr)

head(bulkRNA_meta)

Annotating and fixing the RNAseq data

There are two small issues we need to address:

  • annotation with chromosome, start/end, strand, and gene information
  • fixing ±Inf values

Fixing infinite values

cat("\nThere are a couple of samples with infinite gene counts.\n")

There are a couple of samples with infinite gene counts.
temp <- bulkRNA_counts_raw_qc_umicorr %>% mutate_if(is.numeric, as.integer)
Warning: Problem with `mutate()` column `ae2341`.
ℹ `ae2341 = .Primitive("as.integer")(ae2341)`.
ℹ NAs introduced by coercion to integer range
Warning: Problem with `mutate()` column `ae3078`.
ℹ `ae3078 = .Primitive("as.integer")(ae3078)`.
ℹ NAs introduced by coercion to integer range
Warning: Problem with `mutate()` column `ae1422`.
ℹ `ae1422 = .Primitive("as.integer")(ae1422)`.
ℹ NAs introduced by coercion to integer range
Warning: Problem with `mutate()` column `ae2305`.
ℹ `ae2305 = .Primitive("as.integer")(ae2305)`.
ℹ NAs introduced by coercion to integer range
Warning: Problem with `mutate()` column `ae1256`.
ℹ `ae1256 = .Primitive("as.integer")(ae1256)`.
ℹ NAs introduced by coercion to integer range
Warning: Problem with `mutate()` column `ae411`.
ℹ `ae411 = .Primitive("as.integer")(ae411)`.
ℹ NAs introduced by coercion to integer range
Warning: Problem with `mutate()` column `ae1227`.
ℹ `ae1227 = .Primitive("as.integer")(ae1227)`.
ℹ NAs introduced by coercion to integer range
summary(bulkRNA_counts_raw_qc_umicorr$ae2341)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      0       0       0     Inf      15     Inf 
summary(bulkRNA_counts_raw_qc_umicorr$ae3078)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      0       0       1     Inf      20     Inf 
summary(bulkRNA_counts_raw_qc_umicorr$ae1422)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      0       0       1     Inf      12     Inf 
summary(bulkRNA_counts_raw_qc_umicorr$ae2305)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      0       0       0     Inf       7     Inf 
summary(bulkRNA_counts_raw_qc_umicorr$ae1256)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      0       0       0     Inf      10     Inf 
summary(bulkRNA_counts_raw_qc_umicorr$ae411)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      0       0       0     Inf      15     Inf 
summary(bulkRNA_counts_raw_qc_umicorr$ae1227)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      0       0       1     Inf      11     Inf 
cat("\nFixing the infinite gene counts.\n")

Fixing the infinite gene counts.
temp <- bulkRNA_counts_raw_qc_umicorr %>%
  mutate(across( # For every column you want...
  starts_with("ae"), # ...change all studynumber
  ~ case_when( 
  . ==  Inf ~ max(.[is.finite(.)]), # +Inf becomes the finite max.
  . == -Inf ~ min(.[is.finite(.)]), # -Inf becomes the finite min.
  TRUE ~ . # Other values stay the same.
  )
  )
  )

Annotating


library("devtools")
devtools::install_github("stephenturner/annotables")
Using github PAT from envvar GITHUB_PAT
Downloading GitHub repo stephenturner/annotables@HEAD
These packages have more recent versions available.
It is recommended to update all of them.
Which would you like to update?

 1: All                              
 2: CRAN packages only               
 3: None                             
 4: rlang    (0.4.12 -> 1.0.2) [CRAN]
 5: glue     (1.6.0  -> 1.6.2) [CRAN]
 6: fansi    (1.0.0  -> 1.0.2) [CRAN]
 7: crayon   (1.4.2  -> 1.5.0) [CRAN]
 8: cli      (3.1.0  -> 3.2.0) [CRAN]
 9: pillar   (1.6.4  -> 1.7.0) [CRAN]
10: magrittr (2.0.1  -> 2.0.2) [CRAN]
3
  
   checking for file ‘/private/var/folders/cj/1vxt4xb11m1cx7wn020f8hww0000gn/T/Rtmp0fcsU4/remotes151b27240de45/stephenturner-annotables-631423c/DESCRIPTION’ ...
  
✓  checking for file ‘/private/var/folders/cj/1vxt4xb11m1cx7wn020f8hww0000gn/T/Rtmp0fcsU4/remotes151b27240de45/stephenturner-annotables-631423c/DESCRIPTION’ (435ms)

  
─  preparing ‘annotables’:

  
   checking DESCRIPTION meta-information ...
  
✓  checking DESCRIPTION meta-information

  
─  checking for LF line-endings in source and make files and shell scripts

  
─  checking for empty or unneeded directories

  
     NB: this package now depends on R (>= 3.5.0)

  
     WARNING: Added dependency on R >= 3.5.0 because serialized objects in
     serialize/load version 3 cannot be read in older versions of R.
     File(s) containing such objects:
       ‘annotables/data/bdgp6.rda’ ‘annotables/data/bdgp6_tx2gene.rda’
       ‘annotables/data/ensembl_version.rda’ ‘annotables/data/galgal5.rda’
       ‘annotables/data/galgal5_tx2gene.rda’ ‘annotables/data/grch37.rda’
       ‘annotables/data/grch37_tx2gene.rda’ ‘annotables/data/grch38.rda’
       ‘annotables/data/grch38_tx2gene.rda’ ‘annotables/data/grcm38.rda’
       ‘annotables/data/grcm38_tx2gene.rda’ ‘annotables/data/mmul801.rda’
       ‘annotables/data/mmul801_tx2gene.rda’ ‘annotables/data/rnor6.rda’
       ‘annotables/data/rnor6_tx2gene.rda’ ‘annotables/data/wbcel235.rda’
       ‘annotables/data/wbcel235_tx2gene.rda’
─  building ‘annotables_0.1.91.tar.gz’

  
   
Installing package into '/Users/swvanderlaan/Library/R/x86_64/4.1/library'
(as 'lib' is unspecified)
* installing *source* package ‘annotables’ ...
** using staged installation
** R
** data
*** moving datasets to lazyload DB
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (annotables)
library(dplyr)
library(annotables)

# Columns of interest
# entrez
# symbol
# chr
# start
# end
# strand
# biotype
# description

cat("\nChecking existence of duplicate ENSEMBL IDs - there shouldn't be any.\n")

Checking existence of duplicate ENSEMBL IDs - there shouldn't be any.
id <- temp$ENSEMBL_gene_ID
id[ id %in% id[duplicated(id)] ]
character(0)
rm(id)
cat("\nAnnotating with b37.\n")

Annotating with b37.
bulkRNA_counts <- temp %>% 
  # arrange(p.adjusted) %>% 
  # head(20) %>% 
  inner_join(grch37, by=c("ENSEMBL_gene_ID"="ensgene")) %>%
  # select(gene, estimate, p.adjusted, symbol, description) %>% 
  relocate(entrez, symbol, chr, start, end, strand, biotype, description, 
           .before = ae1618) %>%
  dplyr::filter(duplicated(ENSEMBL_gene_ID) == FALSE)
head(bulkRNA_counts)


id <- bulkRNA_counts$ENSEMBL_gene_ID
id[ id %in% id[duplicated(id)] ]
character(0)

Clinical data

Loading Athero-Express clinical data that we previously saved in an RDS file.

AEDB.CEA <- readRDS(file = paste0(OUT_loc, "/20220317.HDAC9.AEDB.CEA.RDS"))

Fix STUDY_NUMBER

We will fix the STUDY_NUMBER to match the bulkRNAseq data.


AEDB.CEA$STUDY_NUMBER <- paste0("ae", AEDB.CEA$STUDY_NUMBER)
head(AEDB.CEA$STUDY_NUMBER)
[1] "ae1" "ae2" "ae3" "ae4" "ae5" "ae6"

AERNA

Tidy data

We have collected the clinical data, Athero-Express Biobank Study AEDB and, the UMI-corrected, filtered bulk RNAseq data, bulkRNA_counts and its meta-data, bulkRNA-meta.

Here we will clean up the data and create a SummarizedExperiment() object for downstream analyses anad visualizations.

AEDB.CEA.sampleList <- AEDB.CEA$STUDY_NUMBER

# first 9 columns
# ENSEMBL_gene_ID
# entrez
# symbol
# chr
# start
# end
# strand
# biotype
# description

# match up with meta data of RNAseq experiment
bulkRNA_countsFilt <- bulkRNA_counts %>%
  drop_na(chr) %>%   # remove rows that have no information of start, end, chromosome and/or strand
  dplyr::select(1:9, one_of(sort(as.character(AEDB.CEA.sampleList)))) # select gene expression of only patients in RNA-seq AE df, sort in same order as metadata study_number
dim(bulkRNA_countsFilt)
[1] 59851   620
study_samples_bulkNEW <- colnames(bulkRNA_counts[, -(1:9)])
length(study_samples_bulkNEW)
[1] 656
study_samples_AEDBCEA <- c(AEDB.CEA$STUDY_NUMBER)

setdif_samples_NEWvsAEDBCEA <- setdiff(study_samples_bulkNEW, study_samples_AEDBCEA)
setdif_samples_AEDBCEAvsNEW <- setdiff(study_samples_AEDBCEA, study_samples_bulkNEW)

AEDB_filt <- AEDB.CEA[AEDB.CEA$STUDY_NUMBER %in% setdif_samples_NEWvsAEDBCEA,]
table(AEDB_filt$Artery_summary, AEDB_filt$Gender)
                                                                                         
                                                                                          female male
  No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA      0    0
  carotid (left & right)                                                                       0    0
  femoral/iliac (left, right or both sides)                                                    0    0
  other carotid arteries (common, external)                                                    0    0
  carotid bypass and injury (left, right or both sides)                                        0    0
  aneurysmata (carotid & femoral)                                                              0    0
  aorta                                                                                        0    0
  other arteries (renal, popliteal, vertebral)                                                 0    0
  femoral bypass, angioseal and injury (left, right or both sides)                             0    0
# Cut up bulkRNA_countsFilt into 'assay' and 'ranges' part
counts <- as.data.frame(bulkRNA_countsFilt[,-(1:9)])  ## assay part
counts <- counts %>% mutate_if(is.numeric, as.integer)

rownames(counts) <- bulkRNA_countsFilt$ENSEMBL_gene_ID  ## assign rownames

id <- bulkRNA_countsFilt$ENSEMBL_gene_ID
id[ id %in% id[duplicated(id)] ]
character(0)
bulkRNA_rowRanges <- GRanges(bulkRNA_countsFilt$chr,     ## construct a GRanges object containing 4 columns (seqnames, ranges, strand, seqinfo) plus a metadata colum (feature_id): this will be the 'rowRanges' bit
                     IRanges(bulkRNA_countsFilt$start, bulkRNA_countsFilt$end),
                     strand = bulkRNA_countsFilt$strand,
                     feature_id = bulkRNA_countsFilt$ENSEMBL_gene_ID) #, df$pid)
names(bulkRNA_rowRanges) <- bulkRNA_rowRanges$feature_id

# ?org.Hs.eg.db
# ?AnnotationDb

bulkRNA_rowRanges$symbol <- mapIds(org.Hs.eg.db,
                     keys = bulkRNA_rowRanges$feature_id,
                     column = "SYMBOL",
                     keytype = "ENSEMBL",
                     multiVals = "first")
'select()' returned 1:many mapping between keys and columns
# Reference: https://shiring.github.io/genome/2016/10/23/AnnotationDbi

# gene dataframe for EnsDb.Hsapiens.v86
gene_dataframe_EnsDb <- ensembldb::select(EnsDb.Hsapiens.v86, keys = bulkRNA_rowRanges$feature_id,
                                          columns = c("ENTREZID", "SYMBOL", "GENEBIOTYPE"), keytype = "GENEID")
colnames(gene_dataframe_EnsDb) <- c("Ensembl", "Entrez", "HGNC", "GENEBIOTYPE")
colnames(gene_dataframe_EnsDb) <- paste(colnames(gene_dataframe_EnsDb), "EnsDb86", sep = "_")
head(gene_dataframe_EnsDb)


bulkRNA_rowRanges$GENEBIOTYPE_EnsDb86 <- gene_dataframe_EnsDb$GENEBIOTYPE_EnsDb86[match(bulkRNA_rowRanges$feature_id, gene_dataframe_EnsDb$Ensembl_EnsDb86)]
bulkRNA_rowRanges
GRanges object with 59851 ranges and 3 metadata columns:
                  seqnames              ranges strand |      feature_id      symbol  GENEBIOTYPE_EnsDb86
                     <Rle>           <IRanges>  <Rle> |     <character> <character>          <character>
  ENSG00000000003        X 100627108-100639991      - | ENSG00000000003      TSPAN6       protein_coding
  ENSG00000000419       20   50934867-50959140      - | ENSG00000000419        DPM1       protein_coding
  ENSG00000000457        1 169849631-169894267      - | ENSG00000000457       SCYL3       protein_coding
  ENSG00000000460        1 169662007-169854080      + | ENSG00000000460    C1orf112       protein_coding
  ENSG00000000938        1   27612064-27635185      - | ENSG00000000938         FGR       protein_coding
              ...      ...                 ...    ... .             ...         ...                  ...
  ENSG00000248205        5   17502043-17502363      - | ENSG00000248205        <NA> processed_pseudogene
  ENSG00000259098       15   22258138-22258848      + | ENSG00000259098        <NA> processed_pseudogene
  ENSG00000267090       19   38385522-38386759      + | ENSG00000267090        <NA>            antisense
  ENSG00000279119       17   38727833-38728198      - | ENSG00000279119        <NA>       protein_coding
  ENSG00000271242       13   51195881-51196081      + | ENSG00000271242  PRELID3BP2 processed_pseudogene
  -------
  seqinfo: 338 sequences from an unspecified genome; no seqlengths
# merging the two dataframes by HGNC
# bulkRNA_rowRangesHg19Ensemblb86 <- GRanges(merge(bulkRNA_rowRanges, gene_dataframe_EnsDb, by.x = "feature_id", by.y = "Ensembl_EnsDb86", sort = FALSE, all.x = TRUE))
# names(bulkRNA_rowRangesHg19Ensemblb86) <- bulkRNA_rowRangesHg19Ensemblb86$feature_id
# bulkRNA_rowRangesHg19Ensemblb86

# temp <- as.data.frame(table(bulkRNA_rowRanges$GENEBIOTYPE_EnsDb86))
# colnames(temp) <- c("GeneBiotype", "Count")
# 
# ggpubr::ggbarplot(temp, x = "GeneBiotype", y = "Count",
#                   color = "GeneBiotype", fill = "GeneBiotype",
#                   xlab = "gene type") + 
#   theme(axis.text.x = element_text(angle = 45))
# rm(temp)
# match up with meta data of RNAseq experiment
bulkRNA_meta %<>%
     dplyr::filter(study_number %in% AEDB.CEA.sampleList) # select gene expression of only patients in RNA-seq AE df, sort in same order as metadata study_number

# combine meta data from experiment with clinical data
bulkRNA_meta_clin <- merge(bulkRNA_meta, AEDB.CEA, by.x = "study_number", by.y = "STUDY_NUMBER",
                           sort = FALSE, all.x = TRUE)

bulkRNA_meta_clin %<>%
  # mutate(macrophages = factor(macrophages, levels = c("no staining", "minor staining", "moderate staining", "heavy staining"))) %>% 
  # mutate(smc = factor(smc, levels = c("no staining", "minor staining", "moderate staining", "heavy staining"))) %>% 
  # mutate(calcification = factor(calcification, levels = c("no staining", "minor staining", "moderate staining", "heavy staining"))) %>% 
  # mutate(collagen = factor(collagen, levels = c("no staining", "minor staining", "moderate staining", "heavy staining"))) %>% 
  # mutate(fat = factor(fat, levels = c("no fat", "< 40% fat", "> 40% fat"))) %>% 
  mutate(study_number_row = study_number) %>%
  as.data.frame() %>%
  column_to_rownames("study_number_row")

head(bulkRNA_meta_clin)
dim(bulkRNA_meta_clin)
[1]  650 1714

SummarizedExperiment

We make a SummarizedExperiment for the RNAseq data. We make sure to only include the samples we need based on informed consent and we include only the requested variables.

First, we define the variables we need.


# Baseline table variables
basetable_vars = c("Hospital", "ORyear", "Artery_summary",
                   "Age", "Gender", 
                   # "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   # "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerStatus", "AlcoholUse",
                   "DiabetesStatus", 
                   "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                   "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                   "Symptoms.Update2G", "Symptoms.Update3G",
                   "restenos", "stenose",
                   "CAD_history", "PAOD", "Peripheral.interv", 
                   "EP_composite", "EP_composite_time", "EP_major", "EP_major_time",
                   "MAC_rankNorm", "SMC_rankNorm", "Macrophages.bin", "SMC.bin",
                   "Neutrophils_rankNorm", "MastCells_rankNorm",
                   "IPH.bin", "VesselDensity_rankNorm",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", 
                   "OverallPlaquePhenotype", "Plaque_Vulnerability_Index")

basetable_bin = c("Gender", "Artery_summary",
                  "KDOQI", "BMI_WHO",
                  "SmokerStatus", "AlcoholUse",
                  "DiabetesStatus", 
                  "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                  "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                  "Symptoms.Update2G", "Symptoms.Update3G",
                  "restenos", "stenose",
                  "CAD_history", "PAOD", "Peripheral.interv", 
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", 
                  "OverallPlaquePhenotype", "Plaque_Vulnerability_Index")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con

Next, we are constructing the SummarizedExperiment.

cat("* loading data ...\n")
* loading data ...
# this is all the data passing RNAseq quality control and UMI-corrected
# - includes 656 patients
# - after filtering on informed consent and artery type, the end sample size should be 611
# - after filtering on 'no commercial business' based on informed consent, there are fewer samples: 608
dim(bulkRNA_countsFilt)
[1] 59851   620
dim(counts)
[1] 59851   611
cat("\n* making a SummarizedExperiment ...\n")

* making a SummarizedExperiment ...
cat("  > getting counts\n")
  > getting counts
head(counts)
head(bulkRNA_countsFilt)

cat("  > meta data\n")
  > meta data
temp_coldat <- data.frame(STUDY_NUMBER = names(bulkRNA_countsFilt[,10:620]), 
                          SampleType = "plaque", RNAseqType = "3' RNAseq", 
                          row.names = names(bulkRNA_countsFilt[,10:620]))
cat("  > clinical data\n")
  > clinical data
# bulkRNA_meta_clin_COMMERCIAL <- subset(bulkRNA_meta_clin, select = c("study_number", basetable_vars))
bulkRNA_meta_clin_ACADEMIC <- subset(bulkRNA_meta_clin, select = c("study_number", basetable_vars))

# temp_coldat_clin <- merge(temp_coldat, bulkRNA_meta_clin_COMMERCIAL, by.x = "STUDY_NUMBER", by.y = "study_number", sort = FALSE, all.x = TRUE)
temp_coldat_clin <- merge(temp_coldat, bulkRNA_meta_clin_ACADEMIC, by.x = "STUDY_NUMBER", by.y = "study_number", sort = FALSE, all.x = TRUE)

rownames(temp_coldat_clin) <- temp_coldat_clin$STUDY_NUMBER
dim(temp_coldat_clin)
[1] 611  58
cat("  > construction of the SE\n")
  > construction of the SE
(AERNASE <- SummarizedExperiment(assays = list(counts = as.matrix(counts)),
                                colData = temp_coldat_clin, 
                                rowRanges = bulkRNA_rowRanges,
                                metadata = "Athero-Express Biobank Study bulk RNA sequencing. Sample type: carotid plaques. Technology: CEL2-seq adapted for bulk RNA sequencing, thus 3'-focused. UMI-corrected"))
class: RangedSummarizedExperiment 
dim: 59851 611 
metadata(1): ''
assays(1): counts
rownames(59851): ENSG00000000003 ENSG00000000419 ... ENSG00000279119 ENSG00000271242
rowData names(3): feature_id symbol GENEBIOTYPE_EnsDb86
colnames(611): ae1 ae1026 ... ae998 ae999
colData names(58): STUDY_NUMBER SampleType ... OverallPlaquePhenotype Plaque_Vulnerability_Index
cat("\n* removing intermediate files ...\n")

* removing intermediate files ...
rm(temp_coldat, temp_coldat_clin)

Do the study numbers correspond between metadata and expression data?

## check whether rownames metadata and colnames counts are identical
all(colnames(AERNASE) == colnames(counts))
[1] TRUE

So, now we have raw counts for all patients included in the bulk RNAseq data, with all clinical data annotated to them.

Some of the patients might be missing in certain variables:

# We know that some of the patients of the RNAseq is not included in some variables
which(is.na(AERNASE$Gender)) 

missing_values <- which(is.na(AERNASE$Gender))
missing_values

No need to remove missing samples based on a variable, since we will make a DESeq2 object using an empty model.

(AERNASE <- AERNASE[,])
# (AERNASE <- AERNASE[, -missing_values])
# (se <- se[, se$sex == "male"])

Baseline

Preparation

cat("====================================================================================================")
====================================================================================================
cat("SELECTION THE SHIZZLE")
SELECTION THE SHIZZLE
AERNASEClinData <- as.tibble(colData(AERNASE))
Warning: `as.tibble()` was deprecated in tibble 2.0.0.
Please use `as_tibble()` instead.
The signature and semantics have changed, see `?as_tibble`.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
cat("- sanity checking PRIOR to selection")
- sanity checking PRIOR to selection
library(data.table)
require(labelled)
Loading required package: labelled
ae.gender <- to_factor(AERNASEClinData$Gender)
ae.hospital <- to_factor(AERNASEClinData$Hospital)
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"), useNA = "ifany")
        Hospital
Sex      St. Antonius, Nieuwegein UMC Utrecht
  female                       94          54
  male                        255         208
ae.artery <- to_factor(AERNASEClinData$Artery_summary)
table(ae.artery, ae.gender, dnn = c("Sex", "Artery"), useNA = "ifany")
                                                                                         Artery
Sex                                                                                       female male
  No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA      0    0
  carotid (left & right)                                                                     148  461
  femoral/iliac (left, right or both sides)                                                    0    0
  other carotid arteries (common, external)                                                    0    2
  carotid bypass and injury (left, right or both sides)                                        0    0
  aneurysmata (carotid & femoral)                                                              0    0
  aorta                                                                                        0    0
  other arteries (renal, popliteal, vertebral)                                                 0    0
  femoral bypass, angioseal and injury (left, right or both sides)                             0    0
rm(ae.gender, ae.hospital, ae.artery)

# AERNASEClinData[1:10, 1:10]
dim(AERNASEClinData)
[1] 611  58
# DT::datatable(AERNASEClinData)

Showing the baseline table for the scRNAseq data in 39 CEA patients with informed consent.

cat("===========================================================================================")
===========================================================================================
cat("CREATE BASELINE TABLE")
CREATE BASELINE TABLE
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AERNASEClinData.CEA.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                                  # factorVars = basetable_bin,
                                                  # strata = "Gender",
                                                  data = AERNASEClinData, includeNA = TRUE), 
                                   nonnormal = c(), 
                                   quote = FALSE, showAllLevels = TRUE,
                                   format = "p", 
                                   contDigits = 3)[,1:2]
                                    
                                     level                                                                                   Overall           
  n                                                                                                                               611          
  Hospital (%)                       St. Antonius, Nieuwegein                                                                    57.1          
                                     UMC Utrecht                                                                                 42.9          
  ORyear (%)                         2002                                                                                         5.2          
                                     2003                                                                                        10.0          
                                     2004                                                                                        10.6          
                                     2005                                                                                        13.1          
                                     2006                                                                                        13.3          
                                     2007                                                                                        10.5          
                                     2008                                                                                        10.1          
                                     2009                                                                                        11.0          
                                     2010                                                                                         5.6          
                                     2011                                                                                         5.1          
                                     2012                                                                                         3.6          
                                     2013                                                                                         0.8          
                                     2014                                                                                         0.5          
                                     2015                                                                                         0.5          
                                     2016                                                                                         0.2          
                                     2017                                                                                         0.0          
                                     2018                                                                                         0.0          
                                     2019                                                                                         0.0          
                                     2020                                                                                         0.0          
                                     2021                                                                                         0.0          
                                     2022                                                                                         0.0          
  Artery_summary (%)                 No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA      0.0          
                                     carotid (left & right)                                                                      99.7          
                                     femoral/iliac (left, right or both sides)                                                    0.0          
                                     other carotid arteries (common, external)                                                    0.3          
                                     carotid bypass and injury (left, right or both sides)                                        0.0          
                                     aneurysmata (carotid & femoral)                                                              0.0          
                                     aorta                                                                                        0.0          
                                     other arteries (renal, popliteal, vertebral)                                                 0.0          
                                     femoral bypass, angioseal and injury (left, right or both sides)                             0.0          
  Age (mean (SD))                                                                                                              34.484 (8.897)  
  Gender (%)                         female                                                                                      24.2          
                                     male                                                                                        75.8          
  TC_final (mean (SD))                                                                                                          4.663 (1.259)  
  LDL_final (mean (SD))                                                                                                         2.774 (1.046)  
  HDL_final (mean (SD))                                                                                                         1.147 (0.375)  
  TG_final (mean (SD))                                                                                                          1.605 (0.942)  
  systolic (mean (SD))                                                                                                         65.356 (24.793) 
  diastoli (mean (SD))                                                                                                         43.364 (13.409) 
  GFR_MDRD (mean (SD))                                                                                                       1326.275 (661.445)
  BMI (mean (SD))                                                                                                             667.110 (299.088)
  KDOQI (%)                          Normal kidney function                                                                      18.2          
                                     CKD 2 (Mild)                                                                                55.8          
                                     CKD 3 (Moderate)                                                                            23.2          
                                     CKD 4 (Severe)                                                                               1.5          
                                     CKD 5 (Failure)                                                                              0.0          
                                     <NA>                                                                                         1.3          
  BMI_WHO (%)                        Underweight                                                                                  0.8          
                                     Normal                                                                                      33.2          
                                     Overweight                                                                                  46.2          
                                     Obese                                                                                       14.7          
                                     <NA>                                                                                         5.1          
  SmokerStatus (%)                   Current smoker                                                                              36.0          
                                     Ex-smoker                                                                                   44.0          
                                     Never smoked                                                                                16.2          
                                     <NA>                                                                                         3.8          
  AlcoholUse (%)                     Yes                                                                                         34.0          
                                     <NA>                                                                                        66.0          
  DiabetesStatus (%)                 Diabetes                                                                                    78.4          
                                     <NA>                                                                                        21.6          
  Hypertension.selfreport (%)        no                                                                                          27.3          
                                     yes                                                                                         70.5          
                                     <NA>                                                                                         2.1          
  Hypertension.selfreportdrug (%)    no                                                                                          33.7          
                                     yes                                                                                         63.8          
                                     <NA>                                                                                         2.5          
  Hypertension.composite (%)         no                                                                                          13.3          
                                     yes                                                                                         86.7          
  Hypertension.drugs (%)             no                                                                                          22.9          
                                     yes                                                                                         76.9          
                                     <NA>                                                                                         0.2          
  Med.anticoagulants (%)             no                                                                                          87.7          
                                     yes                                                                                         12.1          
                                     <NA>                                                                                         0.2          
  Med.all.antiplatelet (%)           no                                                                                          10.5          
                                     yes                                                                                         89.4          
                                     <NA>                                                                                         0.2          
  Med.Statin.LLD (%)                 no                                                                                          24.5          
                                     yes                                                                                         75.3          
                                     <NA>                                                                                         0.2          
  Stroke_Dx (%)                      No stroke diagnosed                                                                         75.5          
                                     Stroke diagnosed                                                                            18.0          
                                     <NA>                                                                                         6.5          
  sympt (%)                          Asymptomatic                                                                                13.1          
                                     TIA                                                                                         41.2          
                                     minor stroke                                                                                15.4          
                                     Major stroke                                                                                 9.3          
                                     Amaurosis fugax                                                                             15.9          
                                     Four vessel disease                                                                          0.0          
                                     Vertebrobasilary TIA                                                                         0.2          
                                     Retinal infarction                                                                           1.5          
                                     Symptomatic, but aspecific symtoms                                                           2.6          
                                     Contralateral symptomatic occlusion                                                          0.5          
                                     retinal infarction                                                                           0.2          
                                     armclaudication due to occlusion subclavian artery, CEA needed for bypass                    0.0          
                                     retinal infarction + TIAs                                                                    0.0          
                                     Ocular ischemic syndrome                                                                     0.2          
                                     ischemisch glaucoom                                                                          0.0          
                                     subclavian steal syndrome                                                                    0.0          
                                     TGA                                                                                          0.0          
  Symptoms.5G (%)                    Ocular                                                                                       9.5          
                                     Other                                                                                       19.0          
                                     Retinal infarction                                                                           1.6          
                                     Stroke                                                                                      56.6          
                                     TIA                                                                                         13.3          
  AsymptSympt (%)                    Ocular and others                                                                           30.1          
                                     Symptomatic                                                                                 69.9          
  AsymptSympt2G (%)                  Symptomatic                                                                                100.0          
  Symptoms.Update2G (%)              Symptomatic                                                                                 75.5          
                                     <NA>                                                                                        24.5          
  Symptoms.Update3G (%)              Symptomatic                                                                                 75.5          
                                     <NA>                                                                                        24.5          
  restenos (%)                       de novo                                                                                     95.9          
                                     restenosis                                                                                   1.8          
                                     stenose bij angioseal na PTCA                                                                0.0          
                                     <NA>                                                                                         2.3          
  stenose (%)                        0-49%                                                                                        0.3          
                                     50-70%                                                                                       6.2          
                                     70-90%                                                                                      44.0          
                                     90-99%                                                                                      44.8          
                                     100% (Occlusion)                                                                             0.8          
                                     NA                                                                                           0.0          
                                     50-99%                                                                                       0.2          
                                     70-99%                                                                                       0.0          
                                     99                                                                                           0.0          
                                     <NA>                                                                                         3.6          
  CAD_history (%)                    No history CAD                                                                              66.6          
                                     History CAD                                                                                 33.4          
  PAOD (%)                           no                                                                                          79.5          
                                     yes                                                                                         20.5          
  Peripheral.interv (%)              no                                                                                          84.8          
                                     yes                                                                                         15.2          
  EP_composite (%)                   No composite endpoints                                                                      75.0          
                                     Composite endpoints                                                                         24.5          
                                     <NA>                                                                                         0.5          
  EP_composite_time (mean (SD))                                                                                                 2.655 (1.143)  
  EP_major (%)                       No major events (endpoints)                                                                 86.3          
                                     Major events (endpoints)                                                                    13.3          
                                     <NA>                                                                                         0.5          
  EP_major_time (mean (SD))                                                                                                     2.845 (1.021)  
  MAC_rankNorm (mean (SD))                                                                                                      0.276 (0.961)  
  SMC_rankNorm (mean (SD))                                                                                                     -0.041 (0.930)  
  Macrophages.bin (%)                no/minor                                                                                    42.7          
                                     moderate/heavy                                                                              55.5          
                                     <NA>                                                                                         1.8          
  SMC.bin (%)                        no/minor                                                                                    31.6          
                                     moderate/heavy                                                                              66.6          
                                     <NA>                                                                                         1.8          
  Neutrophils_rankNorm (mean (SD))                                                                                              0.256 (1.020)  
  MastCells_rankNorm (mean (SD))                                                                                               -0.021 (1.025)  
  IPH.bin (%)                        no                                                                                          37.6          
                                     yes                                                                                         60.9          
                                     <NA>                                                                                         1.5          
  VesselDensity_rankNorm (mean (SD))                                                                                            0.139 (0.948)  
  Calc.bin (%)                       no/minor                                                                                    46.3          
                                     moderate/heavy                                                                              52.4          
                                     <NA>                                                                                         1.3          
  Collagen.bin (%)                   no/minor                                                                                    19.1          
                                     moderate/heavy                                                                              79.4          
                                     <NA>                                                                                         1.5          
  Fat.bin_10 (%)                      <10%                                                                                       23.4          
                                      >10%                                                                                       75.3          
                                     <NA>                                                                                         1.3          
  Fat.bin_40 (%)                     <40%                                                                                        68.9          
                                     >40%                                                                                        29.8          
                                     <NA>                                                                                         1.3          
  OverallPlaquePhenotype (%)         atheromatous                                                                                37.3          
                                     fibroatheromatous                                                                           31.3          
                                     fibrous                                                                                      0.0          
                                     <NA>                                                                                        31.4          
  Plaque_Vulnerability_Index (%)     0                                                                                            7.4          
                                     1                                                                                           16.5          
                                     2                                                                                           25.5          
                                     3                                                                                           33.1          
                                     4                                                                                           11.9          
                                     5                                                                                            5.6          

Writing the baseline table to Excel format.

# Write basetable
require(openxlsx)
# write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AERNA.CEA.608pts.after_qc.IC_commercial.BaselineTable..xlsx"), 
#            format(AERNASEClinData.CEA.tableOne, digits = 5, scientific = FALSE) , 
#            rowNames = TRUE, colNames = TRUE, overwrite = TRUE)
# 
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AERNA.CEA.611pts.after_qc.IC_academic.BaselineTable..xlsx"), 
           format(AERNASEClinData.CEA.tableOne, digits = 5, scientific = FALSE) , 
           rowNames = TRUE, colNames = TRUE, overwrite = TRUE)

Expression differences

From here we can analyze whether specific genes differ between groups, or do this for the entire gene set as part of DE analysis, and then select our genes of interest. Let’s start with the former.

Prepare DDS and VSD

The dds raw counts need normalization and log transformation first.

AERNAdds <- DESeqDataSet(AERNASE, design = ~ 1)

# Determine the size factors to use for normalization
AERNAdds <- estimateSizeFactors(AERNAdds)

# sizeFactors(AERNAdds)

# Extract the normalized counts
normalized_counts <- counts(AERNAdds, normalized = TRUE)
# head(normalized_counts)

# Log transform counts for QC
AERNAvsd <- vst(AERNAdds, blind = TRUE)

# There is a message stating the following.
# 
# -- note: fitType='parametric', but the dispersion trend was not well captured by the
#    function: y = a/x + b, and a local regression fit was automatically substituted.
#    specify fitType='local' or 'mean' to avoid this message next time.
#    
# No action is required. 
# 
# For more information check: https://www.biostars.org/p/119115/

Extract data of interest

From here, extract the gene expression values, plus gene identifier, annotate with gene symbol, and select the genes of our interest.

expression_data <- assay(AERNAvsd)

# extract expression values from vsd, including ensembl names
expression_data <- as_tibble(data.frame(gene_ensembl = rowRanges(AERNAvsd)$feature_id, assay(AERNAvsd))) %>%
     mutate_at(vars(c("gene_ensembl")), list(as.character)) ## gene_ensembl needs to be character for annotation to work

# annotations
# gene symbol - via org.Hs.eg.db
# columns(org.Hs.eg.db)
expression_data$symbol <- mapIds(org.Hs.eg.db,
                    keys = expression_data$gene_ensembl,
                    column = "SYMBOL",
                    keytype = "ENSEMBL",
                    multiVals = "first")
'select()' returned 1:many mapping between keys and columns
# tidy and subset
expression_data_sel <- expression_data %>%
     dplyr::select(gene_ensembl, symbol, everything()) %>%
     # filter(symbol == "APOE" | symbol == "TRIB3") %>% # filter APOE and TRIB3
     dplyr::filter(symbol %in% target_genes)

head(expression_data_sel)

# tidy and subset non-selected genes
set.seed(141619)
expression_data_sample <- expression_data %>%
     dplyr::select(gene_ensembl, symbol, everything()) %>%
     sample_n(1000) %>%
     unite(symbol_ensembl, symbol, gene_ensembl, sep = "_", remove = FALSE)

expression_data_sample_mean <- expression_data_sample %>%
  select_if(is.numeric) %>%
  colMeans() %>%
  as_tibble(rownames = "study_number") %>%
  dplyr::rename(expression_value_sample = value)

Furthermore, the expression_data_sel df was gathered into a long form df for annotation with symptoms variables from the vsd object, and later visualization and statistics.

# gather expression_data_sel df into long df form for annotation, plotting and statistics
expression_long <-
     gather(expression_data_sel, key = "study_number", value = "expression_value", -c(gene_ensembl, symbol))

# old school way
# Annotate with smoking variables
# sample_ids <- expression_long$study_number
# mm <- match(expression_long$study_number, rownames(colData(vsd)))
#
# ## Add traits to df
# ## Binary traits
# expression_long$sex <- colData(vsd)$sex[mm]
# expression_long$testosterone <- colData(vsd)$testosterone[mm]
# expression_long$t_e2_ratio <- colData(vsd)$t_e2_ratio[mm]

# new school way
plaque_phenotypes_cat <- c("Macrophages.bin",
                           "SMC.bin",
                           "Calc.bin",
                           "Collagen.bin",
                           "Fat.bin_10", 
                           # "Fat.bin_40",
                           "IPH.bin")

plaque_phenotypes_num <- c("MAC_rankNorm", #"macmean0",
                           "SMC_rankNorm", #"smcmean0",
                           # "MastCells_rankNorm", #"mast_cells_plaque",
                           # "Neutrophils_rankNorm", #"neutrophils",
                           "VesselDensity_rankNorm") #"vessel_density")

expression_long <- expression_long %>%
  left_join(bulkRNA_meta_clin %>% dplyr::select(study_number,
                                       plaque_phenotypes_cat,
                                       plaque_phenotypes_num,
                                       epmajor.3years, epmajor.30days,
                                       AsymptSympt2G,
                                       Gender, Hospital),
            by = "study_number") %>%
  mutate(epmajor_3years_yn = str_replace_all(epmajor.3years, c("Excluded" = "yes", "Included" = "no"))) %>%
  mutate(epmajor.30days_yn = str_replace_all(epmajor.30days, c("Excluded" = "yes", "Included" = "no")))
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(plaque_phenotypes_cat)` instead of `plaque_phenotypes_cat` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(plaque_phenotypes_num)` instead of `plaque_phenotypes_num` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.
head(expression_long)

# expression_long %>%
#   write_tsv("genes_interest_expression.txt")

Gene expression - distribution

Filter genes

In case some genes are not available in our data we could filter them here.

target_genes
[1] "HDAC9"  "TWIST1" "IL6"    "IL1B"  

This code is just an example to filter the list from genes that are not in the data.

# target_genes_rm <- c("AC011294.3", "C6orf195", "C9orf53", "AL137026.1", "DUPD1", "RP11-145E5.5", "PVRL2",
#                      "LINC00841", "LOC100130539")
# 
# temp = target_genes[!target_genes %in% target_genes_rm]
# 
# target_genes_qc <- c(temp)

target_genes_qc <- target_genes

# for debug
# target_genes_qc_replace <- c("LINC01600", "DUSP27", "NECTIN2", "C10orf142", "LINC02881")

Plotting expression

Figure 1: Expression of genes of interest: boxplots

# Make directory for plots
ifelse(!dir.exists(file.path(QC_loc, "/Boxplots")), 
       dir.create(file.path(QC_loc, "/Boxplots")), 
       FALSE)
[1] FALSE
BOX_loc = paste0(QC_loc,"/Boxplots")

for(GENE in target_genes_qc){
  cat(paste0("Plotting expression for ", GENE,".\n"))
  temp <- subset(expression_long, symbol == GENE)
  
  compare_means(expression_value ~ Gender, data = temp)
  
  p1 <- ggpubr::ggboxplot(temp,
                          x = "Gender",
                          y = "expression_value",
                          color = "Gender",
                          palette = "npg",
                          add = "jitter",
                          ylab = paste0("normalized expression ", GENE,"" ),
                          repel = TRUE
                          ) + stat_compare_means()
  #print(p1)
  cat(paste0("Saving image for ", GENE,".\n"))
  
  ggsave(filename = paste0(BOX_loc, "/", Today, ".",GENE,".expression_vs_gender.png"), plot = last_plot())
  ggsave(filename = paste0(BOX_loc, "/", Today, ".",GENE,".expression_vs_gender.pdf"), plot = last_plot())
  
  rm(temp, p1 )
}
Plotting expression for HDAC9.
Saving image for HDAC9.
Saving 7 x 7 in image
Saving 7 x 7 in image
Plotting expression for TWIST1.
Saving image for TWIST1.
Saving 7 x 7 in image
Saving 7 x 7 in image
Plotting expression for IL6.
Saving image for IL6.
Saving 7 x 7 in image
Saving 7 x 7 in image
Plotting expression for IL1B.
Saving image for IL1B.
Saving 7 x 7 in image
Saving 7 x 7 in image

Figure 2A: Expression of genes of interest: histograms

# Make directory for plots
ifelse(!dir.exists(file.path(QC_loc, "/Histograms")), 
       dir.create(file.path(QC_loc, "/Histograms")), 
       FALSE)
[1] FALSE
HISTOGRAM_loc = paste0(QC_loc,"/Histograms")

for(GENE in target_genes_qc){
  # cat(paste0("Plotting expression for ", GENE,".\n"))
  temp <- subset(expression_long, symbol == GENE)
  p1 <- ggpubr::gghistogram(temp,
                          x = "expression_value",
                          y = "..count..",
                          color = "Gender", fill = "Gender",
                          palette = "npg",
                          add = "median",
                          ylab = paste0("normalized expression ", GENE,"" )  
                          )
  # print(p1)
  cat(paste0("Saving image for ", GENE,".\n"))
  ggsave(filename = paste0(HISTOGRAM_loc, "/", Today, ".",GENE,".distribution.png"), plot = last_plot())
  # ggsave(filename = paste0(HISTOGRAM_loc, "/", Today, ".",GENE,".distribution.pdf"), plot = last_plot())

  rm(temp, p1 )
}
Saving image for HDAC9.
Saving 7 x 7 in image
Saving image for TWIST1.
Saving 7 x 7 in image
Saving image for IL6.
Saving 7 x 7 in image
Saving image for IL1B.
Saving 7 x 7 in image

Figure 2B: Expression of genes of interest: density plots

# Make directory for plots
ifelse(!dir.exists(file.path(QC_loc, "/Density")), 
       dir.create(file.path(QC_loc, "/Density")), 
       FALSE)
[1] FALSE
DENSITY_loc = paste0(QC_loc,"/Density")

for(GENE in target_genes_qc){
  # cat(paste0("Plotting expression for ", GENE,".\n"))
  temp <- subset(expression_long, symbol == GENE)
  p1 <- ggpubr::gghistogram(temp,
                          x = "expression_value",
                          y = "..density..",
                          color = "Gender", fill = "Gender",
                          palette = "npg",
                          add = "median",
                          ylab = paste0("normalized expression ", GENE,"" )  
                          )
  # print(p1)
  cat(paste0("Saving image for ", GENE,".\n"))
  ggsave(filename = paste0(DENSITY_loc, "/", Today, ".",GENE,".density.png"), plot = last_plot())
  # ggsave(filename = paste0(DENSITY_loc, "/", Today, ".",GENE,".density.pdf"), plot = last_plot())
  
  rm(temp, p1 )
}
Saving image for HDAC9.
Saving 7 x 7 in image
Saving image for TWIST1.
Saving 7 x 7 in image
Saving image for IL6.
Saving 7 x 7 in image
Saving image for IL1B.
Saving 7 x 7 in image

Compare expression to the expression of a sample of 1,000 genes

Figure 3: comparing expression of genes of interest to mean expression of a sample of 1,000 random genes


expression_wide <- expression_long %>%
  dplyr::select(-gene_ensembl) %>%
  spread(key = symbol, value = expression_value)
# the next 3 lines of code gave an error when selecting for genes_interest, since one of the genes of interest is missing: FGF3 is not in the data set. So, we need to select for the other 15 genes.
# genes_interest <- genes_interest[genes_interest$Symbol %in% unique(expression_long$symbol),]
# target_genes_qc

expression_wide2 <- expression_wide %>%
  left_join(expression_data_sample_mean, by = "study_number") %>%
  dplyr::select(study_number, target_genes_qc, expression_value_sample)
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(target_genes_qc)` instead of `target_genes_qc` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.
expression_long2 <- expression_wide2 %>%
  gather(gene, expression_value, -study_number) %>%
  mutate(gene = str_replace_all(gene, c("expression_value_sample" = "Random genes"))) #%>%
  # mutate(gene = factor(gene, levels = c("Random genes", target_genes_qc)))

mean_1000_genes <- mean(expression_data_sample_mean$expression_value_sample)
# head(expression_long2)
# 

  p1 <- ggpubr::ggboxplot(expression_long2,
                          x = "gene",
                          y = "expression_value",
                          color = uithof_color[16],
                          add = "jitter",
                          add.params = list(size = 3, jitter = 0.3), 
                          ylab = paste0("normalized expression ")
                          ) +
    geom_hline(yintercept = mean_1000_genes, linetype = "dashed", color = uithof_color[26], size = 1.25) + 
    theme(axis.text.x = element_text(size = 18, angle = 45, hjust = 1, vjust = 1), # change orientation of x-axis labels
          axis.text.y = element_text(size = 18),
          axis.title.x = element_text(size = 20),
          axis.title.y = element_text(size = 20)) 
  p1

  
  ggsave(filename = paste0(PLOT_loc, "/", Today, ".TargetExpression_vs_1000genes.png"), plot = last_plot())
Saving 18 x 12 in image
  ggsave(filename = paste0(PLOT_loc, "/", Today, ".TargetExpression_vs_1000genes.pdf"), plot = last_plot())
Saving 18 x 12 in image
  
  rm(p1 )

Heatmaps for genes of interest

If we would put these correlations in one simple and comprehensible figure, we could use a correlation heatmap. Again, correlation coefficients used here are Spearman’s.

Figure 4: correlation heatmap between expression of genes of interest

library(tidyverse)
library(magrittr)

temp <- expression_wide %>%
  column_to_rownames("study_number") %>%
  dplyr::select(target_genes_qc) %>% 
  drop_na() %>% # drop NA 
  Filter(function(x) sd(x) != 0, .) # filter variables with sd = 0

temp.cor <- cor(temp, method = "spearman") 

p1 <- pheatmap(data.matrix(temp.cor), 
               scale = "none",
               cluster_rows = TRUE, 
               cluster_cols = TRUE,
               legend = TRUE,
               fontsize = 18)
p1


ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlations.target_genes.pdf"), plot = p1, height = 15, width = 15)

ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlations.target_genes.png"), plot = p1, height = 15, width = 15)


rm(temp, temp.cor, p1)

Gene target list

We are saving the final list of genes of interest


temp <- subset(expression_data_sel, select = c("gene_ensembl", "symbol"))

fwrite(temp,
       file = paste0(OUT_loc, "/", Today, ".target_list.qc.txt"),
       sep = " ", row.names = FALSE, col.names = TRUE,
       showProgress = TRUE)
rm(temp)

Saving AERNA data

We will create a list of samples that should be included based on CEA, and having the proper informed consent (‘academic’). We will save the SummarizedExperiment as a RDS file for easy loading. The clinical data will also be saved as a separate txt-file.


temp <- as.tibble(subset(colData(AERNASE), select = c("STUDY_NUMBER", "SampleType", "RNAseqType")))

# fwrite(temp,
#        file = paste0(OUT_loc, "/", Today, ".AERNA.CEA.608pts.samplelist.after_qc.IC_commercial.csv"),
#        sep = ",", row.names = FALSE, col.names = TRUE,
#        showProgress = TRUE)
# rm(temp)
# 
# temp <- as.tibble(colData(AERNASE))
# 
# fwrite(temp,
#        file = paste0(OUT_loc, "/", Today, ".AERNA.CEA.608pts.clinicaldata.after_qc.IC_commercial.csv"),
#        sep = ",", row.names = FALSE, col.names = TRUE,
#        showProgress = TRUE)
# rm(temp)

fwrite(temp,
       file = paste0(OUT_loc, "/", Today, ".AERNA.CEA.611pts.samplelist.after_qc.IC_academic.csv"),
       sep = ",", row.names = FALSE, col.names = TRUE,
       showProgress = TRUE)
rm(temp)

temp <- as.tibble(colData(AERNASE))

fwrite(temp,
       file = paste0(OUT_loc, "/", Today, ".AERNA.CEA.611pts.clinicaldata.after_qc.IC_academic.csv"),
       sep = ",", row.names = FALSE, col.names = TRUE,
       showProgress = TRUE)
rm(temp)

AERNASE
class: RangedSummarizedExperiment 
dim: 59851 611 
metadata(1): ''
assays(1): counts
rownames(59851): ENSG00000000003 ENSG00000000419 ... ENSG00000279119 ENSG00000271242
rowData names(3): feature_id symbol GENEBIOTYPE_EnsDb86
colnames(611): ae1 ae1026 ... ae998 ae999
colData names(58): STUDY_NUMBER SampleType ... OverallPlaquePhenotype Plaque_Vulnerability_Index
# saveRDS(AERNASE, file = paste0(OUT_loc, "/", Today, ".AERNA.CEA.608pts.SE.after_qc.IC_commercial.RDS"))
saveRDS(AERNASE, file = paste0(OUT_loc, "/", Today, ".AERNA.CEA.611pts.SE.after_qc.IC_academic.RDS"))

Session information


Version:      v1.0.0
Last update:  2022-03-17
Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description:  Script to load bulk RNA sequencing data, and perform gene expression analyses, and visualisations.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).

**MoSCoW To-Do List**
The things we Must, Should, Could, and Would have given the time we have.
_M_

_S_

_C_

_W_

**Changes log**
* v1.0.0 Inital version. Update to the count data, gene list. Filter samples based on artery operated (CEA) and informed consent. Added heatmap of correlation between target genes. 

sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Monterey 12.2.1

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
 [1] stats4    grid      tools     stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] labelled_2.9.0                          annotables_0.1.91                       EnhancedVolcano_1.12.0                  ggrepel_0.9.1                          
 [5] EnsDb.Hsapiens.v86_2.99.0               ensembldb_2.18.2                        AnnotationFilter_1.18.0                 TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
 [9] mygene_1.30.0                           org.Hs.eg.db_3.14.0                     DESeq2_1.34.0                           SummarizedExperiment_1.24.0            
[13] MatrixGenerics_1.6.0                    matrixStats_0.61.0                      GenomicFeatures_1.46.3                  AnnotationDbi_1.56.2                   
[17] Biobase_2.54.0                          GenomicRanges_1.46.1                    GenomeInfoDb_1.30.0                     IRanges_2.28.0                         
[21] S4Vectors_0.32.3                        BiocGenerics_0.40.0                     UpSetR_1.4.0                            ggpubr_0.4.0                           
[25] forestplot_2.0.1                        checkmate_2.0.0                         magrittr_2.0.1                          pheatmap_1.0.12                        
[29] devtools_2.4.3                          usethis_2.1.5                           BlandAltmanLeh_0.3.1                    sjPlot_2.8.10                          
[33] tableone_0.13.0                         haven_2.4.3                             openxlsx_4.2.5                          eeptools_1.2.4                         
[37] DT_0.20                                 knitr_1.37                              forcats_0.5.1                           stringr_1.4.0                          
[41] purrr_0.3.4                             tibble_3.1.6                            ggplot2_3.3.5                           tidyverse_1.3.1                        
[45] data.table_1.14.2                       naniar_0.6.1                            tidyr_1.1.4                             dplyr_1.0.7                            
[49] optparse_1.7.1                          readr_2.1.1                             pander_0.6.4                            rmarkdown_2.11                         
[53] worcs_0.1.9.1                          

loaded via a namespace (and not attached):
  [1] estimability_1.3         rappdirs_0.3.3           rtracklayer_1.54.0       rticles_0.22             coda_0.19-4              ragg_1.2.1              
  [7] visdat_0.5.3             bit64_4.0.5              multcomp_1.4-18          DelayedArray_0.20.0      rpart_4.1.16             KEGGREST_1.34.0         
 [13] RCurl_1.98-1.5           generics_0.1.1           callr_3.7.0              TH.data_1.1-0            RSQLite_2.2.9            proxy_0.4-26            
 [19] chron_2.3-56             bit_4.0.4                tzdb_0.2.0               xml2_1.3.3               lubridate_1.8.0          ggsci_2.9               
 [25] assertthat_0.2.1         xfun_0.29                hms_1.1.1                evaluate_0.14            fansi_1.0.0              restfulr_0.0.13         
 [31] progress_1.2.2           dbplyr_2.1.1             readxl_1.3.1             DBI_1.1.2                geneplotter_1.72.0       htmlwidgets_1.5.4       
 [37] ellipsis_0.3.2           backports_1.4.1          insight_0.16.0           survey_4.1-1             annotate_1.72.0          biomaRt_2.50.2          
 [43] vctrs_0.3.8              remotes_2.4.2            sjlabelled_1.1.8         abind_1.4-5              cachem_1.0.6             withr_2.4.3             
 [49] sys_3.4                  emmeans_1.7.2            vcd_1.4-9                GenomicAlignments_1.30.0 prettyunits_1.1.1        getopt_1.20.3           
 [55] cluster_2.1.2            lazyeval_0.2.2           crayon_1.4.2             genefilter_1.76.0        labeling_0.4.2           pkgconfig_2.0.3         
 [61] vipor_0.4.5              nlme_3.1-155             pkgload_1.2.4            ProtGenerics_1.26.0      nnet_7.3-17              rlang_0.4.12            
 [67] lifecycle_1.0.1          sandwich_3.0-1           filelock_1.0.2           extrafontdb_1.0          BiocFileCache_2.2.0      modelr_0.1.8            
 [73] ggrastr_1.0.1            cellranger_1.1.0         rprojroot_2.0.2          lmtest_0.9-39            datawizard_0.3.0         Matrix_1.4-0            
 [79] carData_3.0-5            boot_1.3-28              zoo_1.8-9                beeswarm_0.4.0           base64enc_0.1-3          reprex_2.0.1            
 [85] processx_3.5.2           png_0.1-7                rjson_0.2.21             parameters_0.17.0        bitops_1.0-7             KernSmooth_2.23-20      
 [91] Biostrings_2.62.0        blob_1.2.2               maptools_1.1-2           arm_1.12-2               jpeg_0.1-9               rstatix_0.7.0           
 [97] ggeffects_1.1.1          ggsignif_0.6.3           scales_1.1.1             memoise_2.0.1            plyr_1.8.6               zlibbioc_1.40.0         
[103] compiler_4.1.2           BiocIO_1.4.0             ash_1.0-15               RColorBrewer_1.1-2       lme4_1.1-27.1            Rsamtools_2.10.0        
[109] cli_3.1.0                XVector_0.34.0           ps_1.6.0                 htmlTable_2.4.0          Formula_1.2-4            MASS_7.3-54             
[115] tidyselect_1.1.1         stringi_1.7.6            textshaping_0.3.6        proj4_1.0-10.1           mitools_2.4              yaml_2.2.1              
[121] askpass_1.1              locfit_1.5-9.4           latticeExtra_0.6-29      credentials_1.3.2        parallel_4.1.2           rstudioapi_0.13         
[127] foreign_0.8-82           gridExtra_2.3            farver_2.1.0             digest_0.6.29            proto_1.0.0              gert_1.5.0              
[133] Rcpp_1.0.7               prereg_0.5.0             car_3.0-12               broom_0.7.11             ggalt_0.4.0              performance_0.8.0       
[139] httr_1.4.2               effectsize_0.6.0.1       sjstats_0.18.1           colorspace_2.0-2         rvest_1.0.2              XML_3.99-0.8            
[145] fs_1.5.2                 ranger_0.13.1            splines_4.1.2            sp_1.4-6                 systemfonts_1.0.3        sessioninfo_1.2.2       
[151] xtable_1.8-4             jsonlite_1.7.2           nloptr_1.2.2.3           testthat_3.1.1           R6_2.5.1                 Hmisc_4.6-0             
[157] gsubfn_0.7               pillar_1.6.4             htmltools_0.5.2          glue_1.6.0               fastmap_1.1.0            minqa_1.2.4             
[163] BiocParallel_1.28.3      class_7.3-20             codetools_0.2-18         maps_3.4.0               pkgbuild_1.3.1           mvtnorm_1.1-3           
[169] utf8_1.2.2               lattice_0.20-45          sqldf_0.4-11             ggbeeswarm_0.6.0         curl_4.3.2               Rttf2pt1_1.3.9          
[175] zip_2.2.0                openssl_1.4.6            survival_3.3-1           desc_1.4.0               munsell_0.5.0            e1071_1.7-9             
[181] GenomeInfoDbData_1.2.7   sjmisc_2.8.9             gtable_0.3.0             extrafont_0.17           bayestestR_0.11.5       

Saving environment

save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.preparation.RData"))
© 1979-2022 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com swvanderlaan.github.io.
---
title: "Plaque expression levels of _HDAC9_ in association with plaque vulnerability traits and secondary vascular events in patients undergoing carotid endarterectomy: an analysis in the Athero-EXPRESS Biobank."
author: "[Sander W. van der Laan, PhD](https://swvanderlaan.github.io) | @swvanderlaan | s.w.vanderlaan@gmail.com"
date: "`r Sys.Date()`"
output:
  html_notebook:
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 6
    fig_retina: 2
    fig_width: 7
    highlight: tango
    theme: lumen
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
mainfont: Arial
subtitle: "Athero-Express RNA sequencing preparation"
editor_options:
  chunk_output_type: inline
# bibliography: references.bib
# knit: worcs::cite_all
---

# General Setup

```{r setup, include=FALSE}
# We recommend that you prepare your raw data for analysis in 'prepare_data.R',
# and end that file with either open_data(yourdata), or closed_data(yourdata).
# Then, uncomment the line below to load the original or synthetic data
# (whichever is available), to allow anyone to reproduce your code:
# load_data()

# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/', 
                      warning = TRUE, # show warnings during codebook generation
                      message = TRUE, # show messages during codebook generation
                      error = TRUE, # do not interrupt codebook generation in case of errors, 
                                    # usually better for debugging
                      echo = TRUE,  # show R code
                      eval = TRUE)

ggplot2::theme_set(ggplot2::theme_minimal())
# pander::panderOptions("table.split.table", Inf)
library("worcs")
library("rmarkdown")

```

```{r echo = FALSE}
rm(list = ls())
```

```{r LocalSystem, echo = FALSE}
### Operating System Version
### MacBook Pro
ROOT_loc = "/Users/swvanderlaan"

### MacBook Air 
# ROOT_loc = "/Users/slaan3"

### General
GENOMIC_loc = paste0(ROOT_loc, "/OneDrive - UMC Utrecht/Genomics")
AEDB_loc = paste0(GENOMIC_loc, "/Athero-Express/AE-AAA_GS_DBs")
LAB_loc = paste0(GENOMIC_loc, "/LabBusiness")

PROJECT_loc = paste0(ROOT_loc, "/git/CirculatoryHealth/AE_20211201_YAW_SWVANDERLAAN_HDAC9")

# Genetic and genomic data
STORAGE_loc = paste0(ROOT_loc, "/PLINK")
AERNA_loc = paste0(STORAGE_loc, "/_AE_ORIGINALS/AERNA")
AESCRNA_loc = paste0(STORAGE_loc, "/_AE_ORIGINALS/AESCRNA/prepped_data")
AEGSQC_loc = paste0(STORAGE_loc, "/_AE_ORIGINALS/AEGS_COMBINED_QC2018")

### SOME VARIABLES WE NEED DOWN THE LINE
TRAIT_OF_INTEREST = "HDAC9" # Phenotype
PROJECTNAME = "HDAC9"

cat("\nCreate a new analysis directory...\n")
ifelse(!dir.exists(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       dir.create(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       FALSE)
ANALYSIS_loc = paste0(PROJECT_loc,"/",PROJECTNAME)

ifelse(!dir.exists(file.path(ANALYSIS_loc, "/PLOTS")), 
       dir.create(file.path(ANALYSIS_loc, "/PLOTS")), 
       FALSE)
PLOT_loc = paste0(ANALYSIS_loc,"/PLOTS")

ifelse(!dir.exists(file.path(PLOT_loc, "/QC")), 
       dir.create(file.path(PLOT_loc, "/QC")), 
       FALSE)
QC_loc = paste0(PLOT_loc,"/QC")

ifelse(!dir.exists(file.path(ANALYSIS_loc, "/OUTPUT")), 
       dir.create(file.path(ANALYSIS_loc, "/OUTPUT")), 
       FALSE)
OUT_loc = paste0(ANALYSIS_loc, "/OUTPUT")

ifelse(!dir.exists(file.path(ANALYSIS_loc, "/BASELINE")), 
       dir.create(file.path(ANALYSIS_loc, "/BASELINE")), 
       FALSE)
BASELINE_loc = paste0(ANALYSIS_loc, "/BASELINE")


setwd(paste0(PROJECT_loc))
getwd()
list.files()

```

```{r Source functions}
source(paste0(PROJECT_loc, "/scripts/functions.R"))
```

```{r loading_packages, message=FALSE, warning=FALSE}
install.packages.auto("pander")
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")

# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)

install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("eeptools")

install.packages.auto("openxlsx")

install.packages.auto("haven")
install.packages.auto("tableone")
install.packages.auto("sjPlot")

install.packages.auto("BlandAltmanLeh")

# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')

# for plotting
install.packages.auto("pheatmap")
install.packages.auto("forestplot")
install.packages.auto("ggplot2")

install.packages.auto("ggpubr")

install.packages.auto("UpSetR")

devtools::install_github("thomasp85/patchwork")

# for Seurat etc
install.packages.auto("GenomicFeatures")
install.packages.auto("GenomicRanges")
install.packages.auto("SummarizedExperiment")
install.packages.auto("DESeq2")
install.packages.auto("org.Hs.eg.db")
install.packages.auto("mygene")
install.packages.auto("TxDb.Hsapiens.UCSC.hg19.knownGene")
install.packages.auto("org.Hs.eg.db")
install.packages.auto("AnnotationDbi")
install.packages.auto("EnsDb.Hsapiens.v86")
install.packages.auto("EnhancedVolcano")

```

```{r Setting: Colors}

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
###
###	No.	Color			      HEX	(RGB)						              CHR		  MAF/INFO
###---------------------------------------------------------------------------------------
###	1	  yellow			    #FBB820 (251,184,32)				      =>	1		or 1.0>INFO
###	2	  gold			      #F59D10 (245,157,16)				      =>	2		
###	3	  salmon			    #E55738 (229,87,56)				      =>	3		or 0.05<MAF<0.2 or 0.4<INFO<0.6
###	4	  darkpink		    #DB003F ((219,0,63)				      =>	4		
###	5	  lightpink		    #E35493 (227,84,147)				      =>	5		or 0.8<INFO<1.0
###	6	  pink			      #D5267B (213,38,123)				      =>	6		
###	7	  hardpink		    #CC0071 (204,0,113)				      =>	7		
###	8	  lightpurple	    #A8448A (168,68,138)				      =>	8		
###	9	  purple			    #9A3480 (154,52,128)				      =>	9		
###	10	lavendel		    #8D5B9A (141,91,154)				      =>	10		
###	11	bluepurple		  #705296 (112,82,150)				      =>	11		
###	12	purpleblue		  #686AA9 (104,106,169)			      =>	12		
###	13	lightpurpleblue	#6173AD (97,115,173/101,120,180)	=>	13		
###	14	seablue			    #4C81BF (76,129,191)				      =>	14		
###	15	skyblue			    #2F8BC9 (47,139,201)				      =>	15		
###	16	azurblue		    #1290D9 (18,144,217)				      =>	16		or 0.01<MAF<0.05 or 0.2<INFO<0.4
###	17	lightazurblue	  #1396D8 (19,150,216)				      =>	17		
###	18	greenblue		    #15A6C1 (21,166,193)				      =>	18		
###	19	seaweedgreen	  #5EB17F (94,177,127)				      =>	19		
###	20	yellowgreen		  #86B833 (134,184,51)				      =>	20		
###	21	lightmossgreen	#C5D220 (197,210,32)				      =>	21		
###	22	mossgreen		    #9FC228 (159,194,40)				      =>	22		or MAF>0.20 or 0.6<INFO<0.8
###	23	lightgreen	  	#78B113 (120,177,19)				      =>	23/X
###	24	green			      #49A01D (73,160,29)				      =>	24/Y
###	25	grey			      #595A5C (89,90,92)				        =>	25/XY	or MAF<0.01 or 0.0<INFO<0.2
###	26	lightgrey		    #A2A3A4	(162,163,164)			      =>	26/MT
###
###	ADDITIONAL COLORS
###	27	midgrey			#D7D8D7
###	28	verylightgrey	#ECECEC"
###	29	white			#FFFFFF
###	30	black			#000000
###----------------------------------------------------------------------------------------------

uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
                 "#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
                 "#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
                 "#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
                 "#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
                        "#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
                        "#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
                        "#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
                        "#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
                        "#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")
### ----------------------------------------------------------------------------
```

# ERA-CVD 'druggable-MI-targets'

<!-- ![ERA-CVD logo]("Users/swvanderlaan/iCloud/Genomics/Projects/#Druggable-MI-Genes/Administration/ERA-CVD\ Logo_CMYK.jpg") -->

For the ERA-CVD 'druggable-MI-targets' project (grantnumber: 01KL1802) we performed two related RNA sequencing (RNAseq) experiments:

1)  conventional ('bulk') RNAseq using RNA extracted from carotid plaque samples, n ± 700. As of `r Today.Report` all samples have been selected and
RNA has been extracted; quality control (QC) was performed and we have a dataset of 635 samples.

2)  single-cell RNAseq (scRNAseq) of at least n = 40 samples (20 females, 20 males). As of `r Today.Report` data is available of 40 samples (3 females, 15 males), we are extending sampling to get more female samples.

Plaque samples are derived from carotid endarterectomies as part of the [Athero-Express Biobank Study](http:www/atheroexpress.nl) which is an ongoing study in the UMC Utrecht.

# Background

Here we obtain data from the `r TRAIT_OF_INTEREST` in plaques.

```{r targets, message=FALSE, warning=FALSE}
library(openxlsx)

gene_list_df <- read.xlsx(paste0(PROJECT_loc, "/targets/Genes.xlsx"), sheet = "Genes")

target_genes <- unlist(gene_list_df$Gene)
target_genes

```

# Load data

First we will load the data:

-   bulk RNA sequencing (RNAseq) experimental data from carotid plaques
-   Athero-Express clinical data.

## Bulk RNAseq data

Here we load the latest dataset from our Athero-Express bulk RNA experiment d.d. 2021-12-03 mapped to b37 and Ensembl 87.

These bulk RNAseq data are filtered and corrected:

-   UMI corrected
-   unmappable genes are excluded

```{r LoadData}
# bulk RNAseq data
bulkRNA_counts_raw_qc_umicorr <- fread(paste0(AERNA_loc,"/raw_data_bulk/raw_counts_batch1till11_qc_umicorrected.txt"))

# batch information
bulkRNA_meta <- fread(paste0(AERNA_loc,"/raw_data_bulk/metadata_raw_counts_batch1till11.txt"))

```

Quick peek at the counts and meta-data of the RNAseq experiment.

```{r QuickPeek}

head(bulkRNA_counts_raw_qc_umicorr)

head(bulkRNA_meta)
```

### Annotating and fixing the RNAseq data

There are two small issues we need to address:

-   annotation with chromosome, start/end, strand, and gene information
-   fixing ±`Inf` values

#### Fixing infinite values

```{r}
cat("\nThere are a couple of samples with infinite gene counts.\n")
temp <- bulkRNA_counts_raw_qc_umicorr %>% mutate_if(is.numeric, as.integer)
summary(bulkRNA_counts_raw_qc_umicorr$ae2341)
summary(bulkRNA_counts_raw_qc_umicorr$ae3078)
summary(bulkRNA_counts_raw_qc_umicorr$ae1422)
summary(bulkRNA_counts_raw_qc_umicorr$ae2305)
summary(bulkRNA_counts_raw_qc_umicorr$ae1256)
summary(bulkRNA_counts_raw_qc_umicorr$ae411)
summary(bulkRNA_counts_raw_qc_umicorr$ae1227)

cat("\nFixing the infinite gene counts.\n")
temp <- bulkRNA_counts_raw_qc_umicorr %>%
  mutate(across( # For every column you want...
  starts_with("ae"), # ...change all studynumber
  ~ case_when( 
  . ==  Inf ~ max(.[is.finite(.)]), # +Inf becomes the finite max.
  . == -Inf ~ min(.[is.finite(.)]), # -Inf becomes the finite min.
  TRUE ~ . # Other values stay the same.
  )
  )
  )


```

#### Annotating

```{r}

library("devtools")
devtools::install_github("stephenturner/annotables")
library(dplyr)
library(annotables)

# Columns of interest
# entrez
# symbol
# chr
# start
# end
# strand
# biotype
# description

cat("\nChecking existence of duplicate ENSEMBL IDs - there shouldn't be any.\n")
id <- temp$ENSEMBL_gene_ID
id[ id %in% id[duplicated(id)] ]
rm(id)
```

```{r}
cat("\nAnnotating with b37.\n")
bulkRNA_counts <- temp %>% 
  # arrange(p.adjusted) %>% 
  # head(20) %>% 
  inner_join(grch37, by=c("ENSEMBL_gene_ID"="ensgene")) %>%
  # select(gene, estimate, p.adjusted, symbol, description) %>% 
  relocate(entrez, symbol, chr, start, end, strand, biotype, description, 
           .before = ae1618) %>%
  dplyr::filter(duplicated(ENSEMBL_gene_ID) == FALSE)
head(bulkRNA_counts)


id <- bulkRNA_counts$ENSEMBL_gene_ID
id[ id %in% id[duplicated(id)] ]

```

## Clinical data

Loading Athero-Express clinical data that we previously saved in an RDS file.

```{r LoadAEDB}
AEDB.CEA <- readRDS(file = paste0(OUT_loc, "/20220317.HDAC9.AEDB.CEA.RDS"))

```

### Fix STUDY_NUMBER

We will fix the `STUDY_NUMBER` to match the bulkRNAseq data.

```{r FixStudyNumber}

AEDB.CEA$STUDY_NUMBER <- paste0("ae", AEDB.CEA$STUDY_NUMBER)
head(AEDB.CEA$STUDY_NUMBER)

```


# AERNA

## Tidy data

We have collected the clinical data, Athero-Express Biobank Study `AEDB` and, the UMI-corrected, filtered bulk RNAseq data, `bulkRNA_counts` and its meta-data, `bulkRNA-meta`.

Here we will clean up the data and create a `SummarizedExperiment()` object for downstream analyses anad visualizations.

```{r Parsing RNAseq, message=FALSE, warning=FALSE}
AEDB.CEA.sampleList <- AEDB.CEA$STUDY_NUMBER

# first 9 columns
# ENSEMBL_gene_ID
# entrez
# symbol
# chr
# start
# end
# strand
# biotype
# description

# match up with meta data of RNAseq experiment
bulkRNA_countsFilt <- bulkRNA_counts %>%
  drop_na(chr) %>%   # remove rows that have no information of start, end, chromosome and/or strand
  dplyr::select(1:9, one_of(sort(as.character(AEDB.CEA.sampleList)))) # select gene expression of only patients in RNA-seq AE df, sort in same order as metadata study_number
dim(bulkRNA_countsFilt)

study_samples_bulkNEW <- colnames(bulkRNA_counts[, -(1:9)])
length(study_samples_bulkNEW)
study_samples_AEDBCEA <- c(AEDB.CEA$STUDY_NUMBER)

setdif_samples_NEWvsAEDBCEA <- setdiff(study_samples_bulkNEW, study_samples_AEDBCEA)
setdif_samples_AEDBCEAvsNEW <- setdiff(study_samples_AEDBCEA, study_samples_bulkNEW)

AEDB_filt <- AEDB.CEA[AEDB.CEA$STUDY_NUMBER %in% setdif_samples_NEWvsAEDBCEA,]
table(AEDB_filt$Artery_summary, AEDB_filt$Gender)

# Cut up bulkRNA_countsFilt into 'assay' and 'ranges' part
counts <- as.data.frame(bulkRNA_countsFilt[,-(1:9)])  ## assay part
counts <- counts %>% mutate_if(is.numeric, as.integer)

rownames(counts) <- bulkRNA_countsFilt$ENSEMBL_gene_ID  ## assign rownames

id <- bulkRNA_countsFilt$ENSEMBL_gene_ID
id[ id %in% id[duplicated(id)] ]

bulkRNA_rowRanges <- GRanges(bulkRNA_countsFilt$chr,	 ## construct a GRanges object containing 4 columns (seqnames, ranges, strand, seqinfo) plus a metadata colum (feature_id): this will be the 'rowRanges' bit
                     IRanges(bulkRNA_countsFilt$start, bulkRNA_countsFilt$end),
                     strand = bulkRNA_countsFilt$strand,
                     feature_id = bulkRNA_countsFilt$ENSEMBL_gene_ID) #, df$pid)
names(bulkRNA_rowRanges) <- bulkRNA_rowRanges$feature_id

# ?org.Hs.eg.db
# ?AnnotationDb

bulkRNA_rowRanges$symbol <- mapIds(org.Hs.eg.db,
                     keys = bulkRNA_rowRanges$feature_id,
                     column = "SYMBOL",
                     keytype = "ENSEMBL",
                     multiVals = "first")

# Reference: https://shiring.github.io/genome/2016/10/23/AnnotationDbi

# gene dataframe for EnsDb.Hsapiens.v86
gene_dataframe_EnsDb <- ensembldb::select(EnsDb.Hsapiens.v86, keys = bulkRNA_rowRanges$feature_id,
                                          columns = c("ENTREZID", "SYMBOL", "GENEBIOTYPE"), keytype = "GENEID")
colnames(gene_dataframe_EnsDb) <- c("Ensembl", "Entrez", "HGNC", "GENEBIOTYPE")
colnames(gene_dataframe_EnsDb) <- paste(colnames(gene_dataframe_EnsDb), "EnsDb86", sep = "_")
head(gene_dataframe_EnsDb)


bulkRNA_rowRanges$GENEBIOTYPE_EnsDb86 <- gene_dataframe_EnsDb$GENEBIOTYPE_EnsDb86[match(bulkRNA_rowRanges$feature_id, gene_dataframe_EnsDb$Ensembl_EnsDb86)]
bulkRNA_rowRanges

# merging the two dataframes by HGNC
# bulkRNA_rowRangesHg19Ensemblb86 <- GRanges(merge(bulkRNA_rowRanges, gene_dataframe_EnsDb, by.x = "feature_id", by.y = "Ensembl_EnsDb86", sort = FALSE, all.x = TRUE))
# names(bulkRNA_rowRangesHg19Ensemblb86) <- bulkRNA_rowRangesHg19Ensemblb86$feature_id
# bulkRNA_rowRangesHg19Ensemblb86

# temp <- as.data.frame(table(bulkRNA_rowRanges$GENEBIOTYPE_EnsDb86))
# colnames(temp) <- c("GeneBiotype", "Count")
# 
# ggpubr::ggbarplot(temp, x = "GeneBiotype", y = "Count",
#                   color = "GeneBiotype", fill = "GeneBiotype",
#                   xlab = "gene type") + 
#   theme(axis.text.x = element_text(angle = 45))
# rm(temp)

```

```{r Parse ClinicalData RNAseq}
# match up with meta data of RNAseq experiment
bulkRNA_meta %<>%
     dplyr::filter(study_number %in% AEDB.CEA.sampleList) # select gene expression of only patients in RNA-seq AE df, sort in same order as metadata study_number

# combine meta data from experiment with clinical data
bulkRNA_meta_clin <- merge(bulkRNA_meta, AEDB.CEA, by.x = "study_number", by.y = "STUDY_NUMBER",
                           sort = FALSE, all.x = TRUE)

bulkRNA_meta_clin %<>%
  # mutate(macrophages = factor(macrophages, levels = c("no staining", "minor staining", "moderate staining", "heavy staining"))) %>% 
  # mutate(smc = factor(smc, levels = c("no staining", "minor staining", "moderate staining", "heavy staining"))) %>% 
  # mutate(calcification = factor(calcification, levels = c("no staining", "minor staining", "moderate staining", "heavy staining"))) %>% 
  # mutate(collagen = factor(collagen, levels = c("no staining", "minor staining", "moderate staining", "heavy staining"))) %>% 
  # mutate(fat = factor(fat, levels = c("no fat", "< 40% fat", "> 40% fat"))) %>% 
  mutate(study_number_row = study_number) %>%
  as.data.frame() %>%
  column_to_rownames("study_number_row")

head(bulkRNA_meta_clin)
dim(bulkRNA_meta_clin)

```

## SummarizedExperiment

We make a `SummarizedExperiment` for the RNAseq data. We make sure to only include the samples we need based on informed consent and we include only the requested variables.

First, we define the variables we need.

```{r}

# Baseline table variables
basetable_vars = c("Hospital", "ORyear", "Artery_summary",
                   "Age", "Gender", 
                   # "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   # "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerStatus", "AlcoholUse",
                   "DiabetesStatus", 
                   "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                   "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                   "Symptoms.Update2G", "Symptoms.Update3G",
                   "restenos", "stenose",
                   "CAD_history", "PAOD", "Peripheral.interv", 
                   "EP_composite", "EP_composite_time", "EP_major", "EP_major_time",
                   "MAC_rankNorm", "SMC_rankNorm", "Macrophages.bin", "SMC.bin",
                   "Neutrophils_rankNorm", "MastCells_rankNorm",
                   "IPH.bin", "VesselDensity_rankNorm",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", 
                   "OverallPlaquePhenotype", "Plaque_Vulnerability_Index")

basetable_bin = c("Gender", "Artery_summary",
                  "KDOQI", "BMI_WHO",
                  "SmokerStatus", "AlcoholUse",
                  "DiabetesStatus", 
                  "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                  "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                  "Symptoms.Update2G", "Symptoms.Update3G",
                  "restenos", "stenose",
                  "CAD_history", "PAOD", "Peripheral.interv", 
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", 
                  "OverallPlaquePhenotype", "Plaque_Vulnerability_Index")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con

```


Next, we are constructing the `SummarizedExperiment`.
```{r RNAseq to SE}
cat("* loading data ...\n")

# this is all the data passing RNAseq quality control and UMI-corrected
# - includes 656 patients
# - after filtering on informed consent and artery type, the end sample size should be 611
# - after filtering on 'no commercial business' based on informed consent, there are fewer samples: 608
dim(bulkRNA_countsFilt)
dim(counts)
cat("\n* making a SummarizedExperiment ...\n")
cat("  > getting counts\n")
head(counts)
head(bulkRNA_countsFilt)

cat("  > meta data\n")
temp_coldat <- data.frame(STUDY_NUMBER = names(bulkRNA_countsFilt[,10:620]), 
                          SampleType = "plaque", RNAseqType = "3' RNAseq", 
                          row.names = names(bulkRNA_countsFilt[,10:620]))
cat("  > clinical data\n")
# bulkRNA_meta_clin_COMMERCIAL <- subset(bulkRNA_meta_clin, select = c("study_number", basetable_vars))
bulkRNA_meta_clin_ACADEMIC <- subset(bulkRNA_meta_clin, select = c("study_number", basetable_vars))

# temp_coldat_clin <- merge(temp_coldat, bulkRNA_meta_clin_COMMERCIAL, by.x = "STUDY_NUMBER", by.y = "study_number", sort = FALSE, all.x = TRUE)
temp_coldat_clin <- merge(temp_coldat, bulkRNA_meta_clin_ACADEMIC, by.x = "STUDY_NUMBER", by.y = "study_number", sort = FALSE, all.x = TRUE)

rownames(temp_coldat_clin) <- temp_coldat_clin$STUDY_NUMBER
dim(temp_coldat_clin)

cat("  > construction of the SE\n")
(AERNASE <- SummarizedExperiment(assays = list(counts = as.matrix(counts)),
                                colData = temp_coldat_clin, 
                                rowRanges = bulkRNA_rowRanges,
                                metadata = "Athero-Express Biobank Study bulk RNA sequencing. Sample type: carotid plaques. Technology: CEL2-seq adapted for bulk RNA sequencing, thus 3'-focused. UMI-corrected"))

cat("\n* removing intermediate files ...\n")
rm(temp_coldat, temp_coldat_clin)

```

Do the study numbers correspond between metadata and expression data?

```{r matching_names}
## check whether rownames metadata and colnames counts are identical
all(colnames(AERNASE) == colnames(counts))

```

So, now we have raw counts for all patients included in the bulk RNAseq data,
with all clinical data annotated to them.

Some of the patients might be missing in certain variables:

```{r missing_values, eval = FALSE}
# We know that some of the patients of the RNAseq is not included in some variables
which(is.na(AERNASE$Gender)) 

missing_values <- which(is.na(AERNASE$Gender))
missing_values
```

No need to remove missing samples based on a variable, since we will make a
DESeq2 object using an empty model.

```{r remove_missing, eval = FALSE}
(AERNASE <- AERNASE[,])
# (AERNASE <- AERNASE[, -missing_values])
# (se <- se[, se$sex == "male"])


```

## Baseline

### Preparation

```{r }
cat("====================================================================================================")
cat("SELECTION THE SHIZZLE")
AERNASEClinData <- as.tibble(colData(AERNASE))

cat("- sanity checking PRIOR to selection")
library(data.table)
require(labelled)
ae.gender <- to_factor(AERNASEClinData$Gender)
ae.hospital <- to_factor(AERNASEClinData$Hospital)
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"), useNA = "ifany")

ae.artery <- to_factor(AERNASEClinData$Artery_summary)
table(ae.artery, ae.gender, dnn = c("Sex", "Artery"), useNA = "ifany")

rm(ae.gender, ae.hospital, ae.artery)

# AERNASEClinData[1:10, 1:10]
dim(AERNASEClinData)
# DT::datatable(AERNASEClinData)

```


Showing the baseline table for the scRNAseq data in 39 CEA patients with
informed consent.

```{r Baseline: Visualize}
cat("===========================================================================================")
cat("CREATE BASELINE TABLE")

# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AERNASEClinData.CEA.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                                  # factorVars = basetable_bin,
                                                  # strata = "Gender",
                                                  data = AERNASEClinData, includeNA = TRUE), 
                                   nonnormal = c(), 
                                   quote = FALSE, showAllLevels = TRUE,
                                   format = "p", 
                                   contDigits = 3)[,1:2]

```

Writing the baseline table to Excel format.

```{r }
# Write basetable
require(openxlsx)
# write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AERNA.CEA.608pts.after_qc.IC_commercial.BaselineTable..xlsx"), 
#            format(AERNASEClinData.CEA.tableOne, digits = 5, scientific = FALSE) , 
#            rowNames = TRUE, colNames = TRUE, overwrite = TRUE)
# 
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AERNA.CEA.611pts.after_qc.IC_academic.BaselineTable..xlsx"), 
           format(AERNASEClinData.CEA.tableOne, digits = 5, scientific = FALSE) , 
           rowNames = TRUE, colNames = TRUE, overwrite = TRUE)

```



# Expression differences

From here we can analyze whether specific genes differ between groups, or do this for the entire gene set as part of DE analysis, and then select our genes of interest. Let's start with the former.

## Prepare DDS and VSD

The dds raw counts need normalization and log transformation first.

```{r model_exploration, cache = TRUE}
AERNAdds <- DESeqDataSet(AERNASE, design = ~ 1)

# Determine the size factors to use for normalization
AERNAdds <- estimateSizeFactors(AERNAdds)

# sizeFactors(AERNAdds)

# Extract the normalized counts
normalized_counts <- counts(AERNAdds, normalized = TRUE)
# head(normalized_counts)

# Log transform counts for QC
AERNAvsd <- vst(AERNAdds, blind = TRUE)

# There is a message stating the following.
# 
# -- note: fitType='parametric', but the dispersion trend was not well captured by the
#    function: y = a/x + b, and a local regression fit was automatically substituted.
#    specify fitType='local' or 'mean' to avoid this message next time.
#    
# No action is required. 
# 
# For more information check: https://www.biostars.org/p/119115/

```

## Extract data of interest

From here, extract the gene expression values, plus gene identifier, annotate with gene symbol, and select the genes of our interest.

```{r expression_data_selection}
expression_data <- assay(AERNAvsd)

# extract expression values from vsd, including ensembl names
expression_data <- as_tibble(data.frame(gene_ensembl = rowRanges(AERNAvsd)$feature_id, assay(AERNAvsd))) %>%
     mutate_at(vars(c("gene_ensembl")), list(as.character)) ## gene_ensembl needs to be character for annotation to work

# annotations
# gene symbol - via org.Hs.eg.db
# columns(org.Hs.eg.db)
expression_data$symbol <- mapIds(org.Hs.eg.db,
                    keys = expression_data$gene_ensembl,
                    column = "SYMBOL",
                    keytype = "ENSEMBL",
                    multiVals = "first")

# tidy and subset
expression_data_sel <- expression_data %>%
     dplyr::select(gene_ensembl, symbol, everything()) %>%
     # filter(symbol == "APOE" | symbol == "TRIB3") %>% # filter APOE and TRIB3
     dplyr::filter(symbol %in% target_genes)

head(expression_data_sel)

# tidy and subset non-selected genes
set.seed(141619)
expression_data_sample <- expression_data %>%
     dplyr::select(gene_ensembl, symbol, everything()) %>%
     sample_n(1000) %>%
     unite(symbol_ensembl, symbol, gene_ensembl, sep = "_", remove = FALSE)

expression_data_sample_mean <- expression_data_sample %>%
  select_if(is.numeric) %>%
  colMeans() %>%
  as_tibble(rownames = "study_number") %>%
  dplyr::rename(expression_value_sample = value)

```

Furthermore, the expression_data_sel df was gathered into a long form `df` for annotation with symptoms variables from the `vsd` object, and later visualization and statistics.

```{r gather}
# gather expression_data_sel df into long df form for annotation, plotting and statistics
expression_long <-
     gather(expression_data_sel, key = "study_number", value = "expression_value", -c(gene_ensembl, symbol))

# old school way
# Annotate with smoking variables
# sample_ids <- expression_long$study_number
# mm <- match(expression_long$study_number, rownames(colData(vsd)))
#
# ## Add traits to df
# ## Binary traits
# expression_long$sex <- colData(vsd)$sex[mm]
# expression_long$testosterone <- colData(vsd)$testosterone[mm]
# expression_long$t_e2_ratio <- colData(vsd)$t_e2_ratio[mm]

# new school way
plaque_phenotypes_cat <- c("Macrophages.bin",
                           "SMC.bin",
                           "Calc.bin",
                           "Collagen.bin",
                           "Fat.bin_10", 
                           # "Fat.bin_40",
                           "IPH.bin")

plaque_phenotypes_num <- c("MAC_rankNorm", #"macmean0",
                           "SMC_rankNorm", #"smcmean0",
                           # "MastCells_rankNorm", #"mast_cells_plaque",
                           # "Neutrophils_rankNorm", #"neutrophils",
                           "VesselDensity_rankNorm") #"vessel_density")

expression_long <- expression_long %>%
  left_join(bulkRNA_meta_clin %>% dplyr::select(study_number,
                                       plaque_phenotypes_cat,
                                       plaque_phenotypes_num,
                                       epmajor.3years, epmajor.30days,
                                       AsymptSympt2G,
                                       Gender, Hospital),
            by = "study_number") %>%
  mutate(epmajor_3years_yn = str_replace_all(epmajor.3years, c("Excluded" = "yes", "Included" = "no"))) %>%
  mutate(epmajor.30days_yn = str_replace_all(epmajor.30days, c("Excluded" = "yes", "Included" = "no")))

head(expression_long)

# expression_long %>%
#   write_tsv("genes_interest_expression.txt")

```

## Gene expression - distribution

### Filter genes

In case some genes are not available in our data we could filter them here.

```{r list target genes}
target_genes
```

This code is just an example to filter the list from genes that are not in the data.

```{r filter target genes}
# target_genes_rm <- c("AC011294.3", "C6orf195", "C9orf53", "AL137026.1", "DUPD1", "RP11-145E5.5", "PVRL2",
#                      "LINC00841", "LOC100130539")
# 
# temp = target_genes[!target_genes %in% target_genes_rm]
# 
# target_genes_qc <- c(temp)

target_genes_qc <- target_genes

# for debug
# target_genes_qc_replace <- c("LINC01600", "DUSP27", "NECTIN2", "C10orf142", "LINC02881")


```

### Plotting expression

**Figure 1: Expression of genes of interest: boxplots**

```{r boxplots_expression}
# Make directory for plots
ifelse(!dir.exists(file.path(QC_loc, "/Boxplots")), 
       dir.create(file.path(QC_loc, "/Boxplots")), 
       FALSE)
BOX_loc = paste0(QC_loc,"/Boxplots")

for(GENE in target_genes_qc){
  cat(paste0("Plotting expression for ", GENE,".\n"))
  temp <- subset(expression_long, symbol == GENE)
  
  compare_means(expression_value ~ Gender, data = temp)
  
  p1 <- ggpubr::ggboxplot(temp,
                          x = "Gender",
                          y = "expression_value",
                          color = "Gender",
                          palette = "npg",
                          add = "jitter",
                          ylab = paste0("normalized expression ", GENE,"" ),
                          repel = TRUE
                          ) + stat_compare_means()
  #print(p1)
  cat(paste0("Saving image for ", GENE,".\n"))
  
  ggsave(filename = paste0(BOX_loc, "/", Today, ".",GENE,".expression_vs_gender.png"), plot = last_plot())
  ggsave(filename = paste0(BOX_loc, "/", Today, ".",GENE,".expression_vs_gender.pdf"), plot = last_plot())
  
  rm(temp, p1 )
}

```

**Figure 2A: Expression of genes of interest: histograms**

```{r hist_expression, message=FALSE, warning=FALSE}
# Make directory for plots
ifelse(!dir.exists(file.path(QC_loc, "/Histograms")), 
       dir.create(file.path(QC_loc, "/Histograms")), 
       FALSE)
HISTOGRAM_loc = paste0(QC_loc,"/Histograms")

for(GENE in target_genes_qc){
  # cat(paste0("Plotting expression for ", GENE,".\n"))
  temp <- subset(expression_long, symbol == GENE)
  p1 <- ggpubr::gghistogram(temp,
                          x = "expression_value",
                          y = "..count..",
                          color = "Gender", fill = "Gender",
                          palette = "npg",
                          add = "median",
                          ylab = paste0("normalized expression ", GENE,"" )  
                          )
  # print(p1)
  cat(paste0("Saving image for ", GENE,".\n"))
  ggsave(filename = paste0(HISTOGRAM_loc, "/", Today, ".",GENE,".distribution.png"), plot = last_plot())
  # ggsave(filename = paste0(HISTOGRAM_loc, "/", Today, ".",GENE,".distribution.pdf"), plot = last_plot())

  rm(temp, p1 )
}

```

**Figure 2B: Expression of genes of interest: density plots**

```{r dens_expression, message=FALSE, warning=FALSE}
# Make directory for plots
ifelse(!dir.exists(file.path(QC_loc, "/Density")), 
       dir.create(file.path(QC_loc, "/Density")), 
       FALSE)
DENSITY_loc = paste0(QC_loc,"/Density")

for(GENE in target_genes_qc){
  # cat(paste0("Plotting expression for ", GENE,".\n"))
  temp <- subset(expression_long, symbol == GENE)
  p1 <- ggpubr::gghistogram(temp,
                          x = "expression_value",
                          y = "..density..",
                          color = "Gender", fill = "Gender",
                          palette = "npg",
                          add = "median",
                          ylab = paste0("normalized expression ", GENE,"" )  
                          )
  # print(p1)
  cat(paste0("Saving image for ", GENE,".\n"))
  ggsave(filename = paste0(DENSITY_loc, "/", Today, ".",GENE,".density.png"), plot = last_plot())
  # ggsave(filename = paste0(DENSITY_loc, "/", Today, ".",GENE,".density.pdf"), plot = last_plot())
  
  rm(temp, p1 )
}

```

## Compare expression to the expression of a sample of 1,000 genes

**Figure 3: comparing expression of genes of interest to mean expression of a sample of 1,000 random genes**

```{r boxplots_expression_comparison, message=FALSE, warning=FALSE}

expression_wide <- expression_long %>%
  dplyr::select(-gene_ensembl) %>%
  spread(key = symbol, value = expression_value)

```

```{r }
# the next 3 lines of code gave an error when selecting for genes_interest, since one of the genes of interest is missing: FGF3 is not in the data set. So, we need to select for the other 15 genes.
# genes_interest <- genes_interest[genes_interest$Symbol %in% unique(expression_long$symbol),]
# target_genes_qc

expression_wide2 <- expression_wide %>%
  left_join(expression_data_sample_mean, by = "study_number") %>%
  dplyr::select(study_number, target_genes_qc, expression_value_sample)

expression_long2 <- expression_wide2 %>%
  gather(gene, expression_value, -study_number) %>%
  mutate(gene = str_replace_all(gene, c("expression_value_sample" = "Random genes"))) #%>%
  # mutate(gene = factor(gene, levels = c("Random genes", target_genes_qc)))

mean_1000_genes <- mean(expression_data_sample_mean$expression_value_sample)
# head(expression_long2)
# 

  p1 <- ggpubr::ggboxplot(expression_long2,
                          x = "gene",
                          y = "expression_value",
                          color = uithof_color[16],
                          add = "jitter",
                          add.params = list(size = 3, jitter = 0.3), 
                          ylab = paste0("normalized expression ")
                          ) +
    geom_hline(yintercept = mean_1000_genes, linetype = "dashed", color = uithof_color[26], size = 1.25) + 
    theme(axis.text.x = element_text(size = 18, angle = 45, hjust = 1, vjust = 1), # change orientation of x-axis labels
          axis.text.y = element_text(size = 18),
          axis.title.x = element_text(size = 20),
          axis.title.y = element_text(size = 20)) 
  p1
  
  ggsave(filename = paste0(PLOT_loc, "/", Today, ".TargetExpression_vs_1000genes.png"), plot = last_plot())
  ggsave(filename = paste0(PLOT_loc, "/", Today, ".TargetExpression_vs_1000genes.pdf"), plot = last_plot())
  
  rm(p1 )


```

## Heatmaps for genes of interest

If we would put these correlations in one simple and comprehensible figure, we could use a correlation heatmap. Again, correlation coefficients used here are Spearman's.

**Figure 4: correlation heatmap between expression of genes of interest**

```{r heatmap_corr_genes, message=FALSE, warning=FALSE}
library(tidyverse)
library(magrittr)

temp <- expression_wide %>%
  column_to_rownames("study_number") %>%
  dplyr::select(target_genes_qc) %>% 
  drop_na() %>% # drop NA 
  Filter(function(x) sd(x) != 0, .) # filter variables with sd = 0

temp.cor <- cor(temp, method = "spearman") 

p1 <- pheatmap(data.matrix(temp.cor), 
               scale = "none",
               cluster_rows = TRUE, 
               cluster_cols = TRUE,
               legend = TRUE,
               fontsize = 18)
p1

ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlations.target_genes.pdf"), plot = p1, height = 15, width = 15)

ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlations.target_genes.png"), plot = p1, height = 15, width = 15)


rm(temp, temp.cor, p1)

```

# Gene target list

We are saving the final list of genes of interest

```{r Save target genes}

temp <- subset(expression_data_sel, select = c("gene_ensembl", "symbol"))

fwrite(temp,
       file = paste0(OUT_loc, "/", Today, ".target_list.qc.txt"),
       sep = " ", row.names = FALSE, col.names = TRUE,
       showProgress = TRUE)
rm(temp)

```

# Saving AERNA data

We will create a list of samples that should be included based on CEA, and having the proper informed consent ('academic'). We will save the `SummarizedExperiment` as a RDS file for easy loading. The clinical data will also be saved as a separate `txt`-file.

```{r}

temp <- as.tibble(subset(colData(AERNASE), select = c("STUDY_NUMBER", "SampleType", "RNAseqType")))

# fwrite(temp,
#        file = paste0(OUT_loc, "/", Today, ".AERNA.CEA.608pts.samplelist.after_qc.IC_commercial.csv"),
#        sep = ",", row.names = FALSE, col.names = TRUE,
#        showProgress = TRUE)
# rm(temp)
# 
# temp <- as.tibble(colData(AERNASE))
# 
# fwrite(temp,
#        file = paste0(OUT_loc, "/", Today, ".AERNA.CEA.608pts.clinicaldata.after_qc.IC_commercial.csv"),
#        sep = ",", row.names = FALSE, col.names = TRUE,
#        showProgress = TRUE)
# rm(temp)

fwrite(temp,
       file = paste0(OUT_loc, "/", Today, ".AERNA.CEA.611pts.samplelist.after_qc.IC_academic.csv"),
       sep = ",", row.names = FALSE, col.names = TRUE,
       showProgress = TRUE)
rm(temp)

temp <- as.tibble(colData(AERNASE))

fwrite(temp,
       file = paste0(OUT_loc, "/", Today, ".AERNA.CEA.611pts.clinicaldata.after_qc.IC_academic.csv"),
       sep = ",", row.names = FALSE, col.names = TRUE,
       showProgress = TRUE)
rm(temp)

```


```{r}

AERNASE

# saveRDS(AERNASE, file = paste0(OUT_loc, "/", Today, ".AERNA.CEA.608pts.SE.after_qc.IC_commercial.RDS"))
saveRDS(AERNASE, file = paste0(OUT_loc, "/", Today, ".AERNA.CEA.611pts.SE.after_qc.IC_academic.RDS"))

```


# Session information

--------------------------------------------------------------------------------

    Version:      v1.0.0
    Last update:  2022-03-17
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to load bulk RNA sequencing data, and perform gene expression analyses, and visualisations.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).

    **MoSCoW To-Do List**
    The things we Must, Should, Could, and Would have given the time we have.
    _M_

    _S_

    _C_

    _W_

    **Changes log**
    * v1.0.0 Inital version. Update to the count data, gene list. Filter samples based on artery operated (CEA) and informed consent. Added heatmap of correlation between target genes. 

--------------------------------------------------------------------------------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment

```{r Saving}
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.preparation.RData"))
```

+---------------------------------------------------------------------------------------------------------------------------------------+
| <sup>© 1979-2022 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com [swvanderlaan.github.io](https://swvanderlaan.github.io).</sup> |
+---------------------------------------------------------------------------------------------------------------------------------------+
